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77 results on '"Laurent Girin"'

1. Make That Sound More 'Metallic': Towards a Perceptually Relevant Control of the Timbre of Synthesizer Sounds Using a Variational Autoencoder

Author: Fanny Roche, Thomas Hueber, Maëva Garnier, Samuel Limier, and Laurent Girin
Subjects: synthesizer sounds, timbre perception and verbal description, variational autoencoders, machine learning, audio synthesis, Information technology, T58.5-58.64, Music, M1-5000
Abstract: In this article, we propose a new method of sound transformation based on control parameters that are intuitive and relevant for musicians. This method uses a variational autoencoder (VAE) model that is first trained in an unsupervised manner on a large dataset of synthesizer sounds. Then, a perceptual regularization term is added to the loss function to be optimized, and a supervised fine-tuning of the model is carried out using a small subset of perceptually labeled sounds. The labels were obtained from a perceptual test of Verbal Attribute Magnitude Estimation in which listeners rated this training sound dataset along eight perceptual dimensions (French equivalents of 'metallic, warm, breathy, vibrating, percussive, resonating, evolving, aggressive'). These dimensions were identified as relevant for the description of synthesizer sounds in a first Free Verbalization test. The resulting VAE model was evaluated by objective reconstruction measures and a perceptual test. Both showed that the model was able, to a certain extent, to capture the acoustic properties of most of the perceptual dimensions and to transform sound timbre along at least two of them ('aggressive' and 'vibrating') in a perceptually relevant manner. Moreover, it was able to generalize to unseen samples even though a small set of labeled sounds was used.
Published: 2021
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2. Expectation-maximisation for speech source separation using convolutive transfer function

Author: Xiaofei Li, Laurent Girin, and Radu Horaud
Subjects: speech enhancement, expectation-maximisation algorithm, transfer functions, speech processing, Fourier transforms, microphone arrays, frequency-domain analysis, blind source separation, microphones, convolution, source separation, reverberation, convolutive transfer function, under-determined speech source separation, multichannel microphone signals, convolutive mixtures, multiple sources, time-domain signals, short-time Fourier, (STFT) domain, room filters, STFT domain, widely used narrowband assumption, CTF coefficients, mixing filters, STFT coefficients, Computational linguistics. Natural language processing, P98-98.5, Computer software, QA76.75-76.765
Abstract: This study addresses the problem of under-determined speech source separation from multichannel microphone signals, i.e. the convolutive mixtures of multiple sources. The time-domain signals are first transformed to the short-time Fourier transform (STFT) domain. To represent the room filters in the STFT domain, instead of the widely used narrowband assumption, the authors propose to use a more accurate model, i.e. the convolutive transfer function (CTF). At each frequency band, the CTF coefficients of the mixing filters and the STFT coefficients of the sources are jointly estimated by maximising the likelihood of the microphone signals, which is resolved by an expectation-maximisation algorithm. Experiments show that the proposed method provides very satisfactory performance under highly reverberant environments.
Published: 2019
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3. Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces.

Author: Florent Bocquelet, Thomas Hueber, Laurent Girin, Christophe Savariaux, and Blaise Yvert
Subjects: Biology (General), QH301-705.5
Abstract: Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech. The articulatory-to-acoustic mapping is performed using a deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded on a reference speaker synchronously with the produced speech signal. This DNN is then used in both offline and online modes to map the position of sensors glued on different speech articulators into acoustic parameters that are further converted into an audio signal using a vocoder. In offline mode, highly intelligible speech could be obtained as assessed by perceptual evaluation performed by 12 listeners. Then, to anticipate future BCI applications, we further assessed the real-time control of the synthesizer by both the reference speaker and new speakers, in a closed-loop paradigm using EMA data recorded in real time. A short calibration period was used to compensate for differences in sensor positions and articulatory differences between new speakers and the reference speaker. We found that real-time synthesis of vowels and consonants was possible with good intelligibility. In conclusion, these results open to future speech BCI applications using such articulatory-based speech synthesizer.
Published: 2016
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4. Adaptive Long-Term Coding of LSF Parameters Trajectories for Large-Delay/Very- to Ultra-Low Bit-Rate Speech Coding

Author: Laurent Girin
Subjects: Acoustics. Sound, QC221-246, Electronic computers. Computer science, QA75.5-76.95
Abstract: This paper presents a model-based method for coding the LSF parameters of LPC speech coders on a “long-term” basis, that is, beyond the usual 20–30 ms frame duration. The objective is to provide efficient LSF quantization for a speech coder with large delay but very- to ultra-low bit-rate (i.e., below 1 kb/s). To do this, speech is first segmented into voiced/unvoiced segments. A Discrete Cosine model of the time trajectory of the LSF vectors is then applied to each segment to capture the LSF interframe correlation over the whole segment. Bi-directional transformation from the model coefficients to a reduced set of LSF vectors enables both efficient “sparse” coding (using here multistage vector quantizers) and the generation of interpolated LSF vectors at the decoder. The proposed method provides up to 50% gain in bit-rate over frame-by-frame quantization while preserving signal quality and competes favorably with 2D-transform coding for the lower range of tested bit rates. Moreover, the implicit time-interpolation nature of the long-term coding process provides this technique a high potential for use in speech synthesis systems.
Published: 2010
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5. Learning and controlling the source-filter representation of speech with a variational autoencoder

Author: Samir Sadok, Simon Leglaive, Laurent Girin, Xavier Alameda-Pineda, Renaud Séguier, Institut d'Électronique et des Technologies du numéRique (IETR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ), CentraleSupélec [campus de Rennes], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Vers des robots à l’intelligence sociale au travers de l’apprentissage, de la perception et de la commande (ROBOTLEARN), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Grenoble Alpes (UGA), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), ANR-19-CE33-0008,ML3RI,Apprentissage de bas-niveau d'ineractions robotiques multi-modales avec plusieurs personnes(2019), European Project: 871245,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),SPRING(2020), Société Française d'Acoustique (SFA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - pôle Sciences et technologie, and Leglaive, Simon
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), Linguistics and Language, Deep generative models, Variational autoencoder, Computer Science - Sound, Language and Linguistics, Representation learning, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Communication, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-SD] Computer Science [cs]/Sound [cs.SD], Computer Science Applications, Source-filter model, Modeling and Simulation, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Computer Vision and Pattern Recognition, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Understanding and controlling latent representations in deep generative models is a challenging yet important problem for analyzing, transforming and generating various types of data. In speech processing, inspiring from the anatomical mechanisms of phonation, the source-filter model considers that speech signals are produced from a few independent and physically meaningful continuous latent factors, among which the fundamental frequency $f_0$ and the formants are of primary importance. In this work, we start from a variational autoencoder (VAE) trained in an unsupervised manner on a large dataset of unlabeled natural speech signals, and we show that the source-filter model of speech production naturally arises as orthogonal subspaces of the VAE latent space. Using only a few seconds of labeled speech signals generated with an artificial speech synthesizer, we propose a method to identify the latent subspaces encoding $f_0$ and the first three formant frequencies, we show that these subspaces are orthogonal, and based on this orthogonality, we develop a method to accurately and independently control the source-filter speech factors within the latent subspaces. Without requiring additional information such as text or human-labeled data, this results in a deep generative model of speech spectrograms that is conditioned on $f_0$ and the formant frequencies, and which is applied to the transformation speech signals. Finally, we also propose a robust $f_0$ estimation method that exploits the projection of a speech signal onto the learned latent subspace associated with $f_0$., 23 pages, 7 figures, companion website: https://samsad35.github.io/site-sfvae/
Published: 2023
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6. A survey of sound source localization with deep learning methods

Author: Pierre-Amaury Grumiaux, Srđan Kitić, Laurent Girin, Alexandre Guérin, Laboratoire des Sciences du Numérique de Nantes (LS2N), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Centrale de Nantes (Nantes Univ - ECN), Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes université - UFR des Sciences et des Techniques (Nantes univ - UFR ST), Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ), Orange Labs [Cesson-Sévigné], Orange Labs, GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), ANRT CIFRE 2019/0533, ANR-19-P3IA-0003, and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Subjects: FOS: Computer and information sciences, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Sound (cs.SD), Computer Science - Machine Learning, Acoustics and Ultrasonics, deep learning, audio processing, Computer Science - Sound, Machine Learning (cs.LG), sound source localization, Arts and Humanities (miscellaneous), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Audio and Speech Processing (eess.AS), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: This article is a survey on deep learning methods for single and multiple sound source localization. We are particularly interested in sound source localization in indoor/domestic environment, where reverberation and diffuse noise are present. We provide an exhaustive topography of the neural-based localization literature in this context, organized according to several aspects: the neural network architecture, the type of input features, the output strategy (classification or regression), the types of data used for model training and evaluation, and the model training strategy. This way, an interested reader can easily comprehend the vast panorama of the deep learning-based sound source localization methods. Tables summarizing the literature survey are provided at the end of the paper for a quick search of methods with a given set of target characteristics., Accepted for publication in The Journal of the Acoustical Society of America
Published: 2022
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7. Dynamical Variational Autoencoders: A Comprehensive Review

Author: Xiaoyu Bie, Laurent Girin, Thomas Hueber, Xavier Alameda-Pineda, Julien Diard, Simon Leglaive, GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Vers des robots à l’intelligence sociale au travers de l’apprentissage, de la perception et de la commande (ROBOTLEARN), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Grenoble Alpes (UGA), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Psychologie et NeuroCognition (LPNC ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), European Project: 871245,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),SPRING(2020), CentraleSupélec, Nantes Université (NU)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), European Project: H2020,SPRING, Université de Nantes (UN)-Université de Rennes 1 (UR1), Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Nantes (UN)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Learning and statistical methods, Computer science, Speech/audio/image/video compression, Time series analysis, Machine Learning (stat.ML), Machine learning, computer.software_genre, Machine Learning (cs.LG), Nonlinear signal processing, Deep Learning, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Statistics - Machine Learning, Artificial Intelligence, State space, Graphical model, Latent variable models, Sequence, business.industry, Dimensionality reduction, Deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Autoencoder, Dynamics, Human-Computer Interaction, Generative model, Recurrent neural network, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Artificial intelligence, Graphical models, business, Variational inference, computer, Software
Abstract: International audience; The Variational Autoencoder (VAE) is a powerful deep generative model that is now extensively used to represent high-dimensional complex data via a low-dimensional latent space that is learned in an unsupervised manner. In the original VAE model, input data vectors are processed independently. In the recent years, a series of papers have presented different extensions of the VAE to sequential data, that not only model the latent space, but also model the temporal dependencies within a sequence of data vectors and/or corresponding latent vectors, relying on recurrent neural networks or state space models. In this paper we perform an extensive literature review of these models. Importantly, we introduce and discuss a general class of models called Dynamical Variational Autoencoders (DVAEs) that encompass a large subset of these temporal VAE extensions. Then we present in details seven different instances of DVAE that were recently proposed in the literature, with an effort to homogenize the notations and presentation lines, as well as to relate those models with existing classical temporal models (that are also presented for the sake of completeness). We reimplemented those seven DVAE models and we present the results of an experimental benchmark that we conducted on the speech analysis-resynthesis task (the PyTorch code will be made publicly available). An extensive discussion is presented at the end of the paper, aiming to comment on important issues concerning the DVAE class of models and to describe future research guidelines.
Published: 2021
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8. Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input

Author: Laurent Girin, Laurent Besacier, Brooke Stephenson, Thomas Hueber, Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole (GETALP), Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Subjects: FOS: Computer and information sciences, Incremental text-to-speech, Computer science, Speech recognition, Context (language use), neural language models, 02 engineering and technology, Measure (mathematics), 030507 speech-language pathology & audiology, 03 medical and health sciences, Naturalness, prosody, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Audio and Speech Processing (eess.AS), 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Prosody, Computer Science - Computation and Language, 020206 networking & telecommunications, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Duration (music), Language model, 0305 other medical science, Computation and Language (cs.CL), Word (computer architecture), Energy (signal processing), Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: The prosody of a spoken word is determined by its surrounding context. In incremental text-to-speech synthesis, where the synthesizer produces an output before it has access to the complete input, the full context is often unknown which can result in a loss of naturalness in the synthesized speech. In this paper, we investigate whether the use of predicted future text can attenuate this loss. We compare several test conditions of next future word: (a) unknown (zero-word), (b) language model predicted, (c) randomly predicted and (d) ground-truth. We measure the prosodic features (pitch, energy and duration) and find that predicted text provides significant improvements over a zero-word lookahead, but only slight gains over random-word lookahead. We confirm these results with a perceptive test., Comment: 4 pages
Published: 2021
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9. Make That Sound More Metallic: Towards a Perceptually Relevant Control of the Timbre of Synthesizer Sounds Using a Variational Autoencoder

Author: Samuel Limier, Maëva Garnier, Fanny Roche, Laurent Girin, Thomas Hueber, GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Arturia [Montbonnot-Saint-Martin], and GIPSA - Perception, Contrôle, Multimodalité et Dynamiques de la parole (GIPSA-PCMD)
Subjects: Computer science, media_common.quotation_subject, Speech recognition, Information technology, timbre perception and verbal description, 01 natural sciences, Regularization (mathematics), 050105 experimental psychology, Transformation (music), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Perception, 0103 physical sciences, 0501 psychology and cognitive sciences, M1-5000, 010301 acoustics, media_common, Sound (medical instrument), [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], 05 social sciences, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences, audio synthesis, Synthesizer sounds, [SHS.ART]Humanities and Social Sciences/Art and art history, T58.5-58.64, Autoencoder, Small set, Term (time), variational autoencoders, machine learning, Timbre, Music
Abstract: International audience; In this article, we propose a new method of sound transformation based on control parameters that are intuitive and relevant for musicians. This method uses a variational autoencoder (VAE) model that is first trained in an unsupervised manner on a large dataset of synthesizer sounds. Then, a perceptual regularization term is added to the loss function to be optimized, and a supervised fine-tuning of the model is carried out using a small subset of perceptually labeled sounds. The labels were obtained from a perceptual test of Verbal Attribute Magnitude Estimation in which listeners rated this training sound dataset along eight perceptual dimensions (French equivalents of metallic, warm, breathy, vibrating, percussive, resonating, evolving, aggressive). These dimensions were identified as relevant for the description of synthesizer sounds in a first Free Verbalization test. The resulting VAE model was evaluated by objective reconstruction measures and a perceptual test. Both showed that the model was able, to a certain extent, to capture the acoustic properties of most of the perceptual dimensions and to transform sound timbre along at least two of them (aggressive and vibrating) in a perceptually relevant manner. Moreover, it was able to generalize to unseen samples even though a small set of labeled sounds was used.
Published: 2021
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10. Learning robust speech representation with an articulatory-regularized variational autoencoder

Author: Laurent Girin, Jean-Luc Schwartz, Marc-Antoine Georges, Thomas Hueber, GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), GIPSA - Perception, Contrôle, Multimodalité et Dynamiques de la parole (GIPSA-PCMD), Laboratoire de Psychologie et NeuroCognition (LPNC ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Speech production, Speech perception, Computer science, speech production, Quantitative Biology::Tissues and Organs, Speech recognition, Physics::Medical Physics, 02 engineering and technology, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Computer Science - Sound, 030507 speech-language pathology & audiology, 03 medical and health sciences, representation learning, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, variational autoencoder, Representation (mathematics), Computer Science - Computation and Language, 020206 networking & telecommunications, Autoencoder, Speech enhancement, Generative model, Computer Science::Graphics, Computer Science::Sound, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], speech enhancement, articulatory model, 0305 other medical science, Computation and Language (cs.CL), Feature learning, Vocal tract, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: International audience; It is increasingly considered that human speech perception and production both rely on articulatory representations. In this paper, we investigate whether this type of representation could improve the performances of a deep generative model (here a variational autoencoder) trained to encode and decode acoustic speech features. First we develop an articulatory model able to associate articulatory parameters describing the jaw, tongue, lips and velum configurations with vocal tract shapes and spectral features. Then we incorporate these articulatory parameters into a variational autoencoder applied on spectral features by using a regularization technique that constrains part of the latent space to represent articulatory trajectories. We show that this articulatory constraint improves model training by decreasing time to convergence and reconstruction loss at convergence, and yields better performance in a speech denoising task.
Published: 2021

11. Reverberant Sound Localization with a Robot Head Based on Direct-Path Relative Transfer Function

Author: Radu Horaud, Laurent Girin, Xiaofei Li, Fabien Badeig, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), IEEE, European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: FOS: Computer and information sciences, Microphone array, Sound (cs.SD), Computer science, Feature vector, Acoustics, 02 engineering and technology, Transfer function, Computer Science - Sound, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, Computer Science - Robotics, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Audio and Speech Processing (eess.AS), 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Impulse response, Short-time Fourier transform, Spectral density, 020206 networking & telecommunications, Noise, Fourier transform, binaural hearing, Computer Science::Sound, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], symbols, sound-source localization, 0305 other medical science, Robotics (cs.RO), Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: This paper addresses the problem of sound-source localization (SSL) with a robot head, which remains a challenge in real-world environments. In particular we are interested in locating speech sources, as they are of high interest for human-robot interaction. The microphone-pair response corresponding to the direct-path sound propagation is a function of the source direction. In practice, this response is contaminated by noise and reverberations. The direct-path relative transfer function (DP-RTF) is defined as the ratio between the direct-path acoustic transfer function (ATF) of the two microphones, and it is an important feature for SSL. We propose a method to estimate the DP-RTF from noisy and reverberant signals in the short-time Fourier transform (STFT) domain. First, the convolutive transfer function (CTF) approximation is adopted to accurately represent the impulse response of the microphone array, and the first coefficient of the CTF is mainly composed of the direct-path ATF. At each frequency, the frame-wise speech auto- and cross-power spectral density (PSD) are obtained by spectral subtraction. Then a set of linear equations is constructed by the speech auto- and cross-PSD of multiple frames, in which the DP-RTF is an unknown variable, and is estimated by solving the equations. Finally, the estimated DP-RTFs are concatenated across frequencies and used as a feature vector for SSL. Experiments with a robot, placed in various reverberant environments, show that the proposed method outperforms two state-of-the-art methods., IEEE/RSJ International Conference on Intelligent Robots and Systems
Published: 2020
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12. What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS

Author: Laurent Girin, Brooke Stephenson, Thomas Hueber, Laurent Besacier, Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole (GETALP), Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Subjects: FOS: Computer and information sciences, Computer Science - Computation and Language, incremental speech synthesis, Computer science, Speech recognition, Contrast (statistics), 020206 networking & telecommunications, Context (language use), Speech synthesis, 02 engineering and technology, MUSHRA, computer.software_genre, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], representation learning, deep neural networks, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Computation and Language (cs.CL), Encoder, Feature learning, computer, Sentence, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: In incremental text to speech synthesis (iTTS), the synthesizer produces an audio output before it has access to the entire input sentence. In this paper, we study the behavior of a neural sequence-to-sequence TTS system when used in an incremental mode, i.e. when generating speech output for token n, the system has access to n + k tokens from the text sequence. We first analyze the impact of this incremental policy on the evolution of the encoder representations of token n for different values of k (the lookahead parameter). The results show that, on average, tokens travel 88% of the way to their full context representation with a one-word lookahead and 94% after 2 words. We then investigate which text features are the most influential on the evolution towards the final representation using a random forest analysis. The results show that the most salient factors are related to token length. We finally evaluate the effects of lookahead k at the decoder level, using a MUSHRA listening test. This test shows results that contrast with the above high figures: speech synthesis quality obtained with 2 word-lookahead is significantly lower than the one obtained with the full sentence., Comment: 5 pages, 4 figures
Published: 2020

13. A Recurrent Variational Autoencoder for Speech Enhancement

Author: Simon Leglaive, Laurent Girin, Radu Horaud, Xavier Alameda-Pineda, CentraleSupélec, Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Interpretation and Modelling of Images and Videos (PERCEPTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), GIPSA Pôle Parole et Cognition (GIPSA-PPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), IEEE, PNRIA, ANR-19-CE33-0008,ML3RI,Apprentissage de bas-niveau d'ineractions robotiques multi-modales avec plusieurs personnes(2019), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Nantes Université (NU)-Université de Rennes 1 (UR1)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Machine Learning, Computer science, Computer Science - Artificial Intelligence, Speech recognition, Recurrent variational autoencoders, Speech enhancement, 02 engineering and technology, Latent variable, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computer Science - Sound, Non-negative matrix factorization, Machine Learning (cs.LG), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Nonnegative matrix factorization, 030507 speech-language pathology & audiology, 03 medical and health sciences, Audio and Speech Processing (eess.AS), 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Neural and Evolutionary Computing (cs.NE), Computer Science - Neural and Evolutionary Computing, 020206 networking & telecommunications, Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing), Autoencoder, Artificial Intelligence (cs.AI), Computer Science::Sound, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Noise (video), 0305 other medical science, Variational inference, Encoder, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Generative grammar, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: International audience; This paper presents a generative approach to speech enhancement based on a recurrent variational autoencoder (RVAE). The deep generative speech model is trained using clean speech signals only, and it is combined with a nonnegative matrix factorization noise model for speech enhancement. We propose a variational expectation-maximization algorithm where the encoder of the RVAE is finetuned at test time, to approximate the distribution of the latent variables given the noisy speech observations. Compared with previous approaches based on feed-forward fully-connected architectures, the proposed recurrent deep generative speech model induces a posterior temporal dynamic over the latent variables, which is shown to improve the speech enhancement results.
Published: 2020
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14. Dynamical Variational Autoencoders : A Comprehensive Review

Author: Laurent Girin, Simon Leglaive, Xiaoyu Bie, Julien Diard, Thomas Hueber, Xavier Alameda-Pineda, Laurent Girin, Simon Leglaive, Xiaoyu Bie, Julien Diard, Thomas Hueber, and Xavier Alameda-Pineda
Subjects: Neural networks (Computer science)
Abstract: Variational autoencoders (VAEs) are powerful deep generative models widely used to represent high-dimensional complex data through a low-dimensional latent space learned in an unsupervised manner. In this monograph the authors introduce and discuss a general class of models, called dynamical variational autoencoders (DVAEs), which extend VAEs to model temporal vector sequences. In doing so the authors provide: •a formal definition of the general class of DVAEs •a detailed and complete technical description of seven DVAE models •a rapid overview of other DVAE models presented in the recent literature •discussion of the recent developments in DVAEs in relation to the history and technical background of the classical models DVAEs are built on •a quantitative benchmark of the selected DVAE models •a discussion to put the DVAE class of models into perspective This monograph is a comprehensive review of the current state-of-the-art in DVAEs. It gives the reader an accessible summary of the technical aspects of the different DVAE models, their connections with classical models, their cross-connections, and their unification in the DVAE class in a concise, easy-to-read book. The authors have put considerable effort into unifying the terminology and notation used across the various models which all students, researchers and practitioners working in machine learning will find an invaluable resource.
Published: 2021

15. Audio-Visual Variational Fusion for Multi-Person Tracking with Robots

Author: Yutong Ban, Guillaume Sarrazin, Xavier Alameda-Pineda, Laurent Girin, Soraya Arias, Guillaume Delorme, Radu Horaud, Xiaofei Li, Bastien Morgue, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Service Expérimentation et Développement (SED [Grenoble]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and SED [Grenoble]
Subjects: Computer science, media_common.quotation_subject, Tracking, Scene understanding, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020206 networking & telecommunications, 02 engineering and technology, Tracking (particle physics), Field (computer science), Presentation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Human–computer interaction, Perception, Vision for robotics, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Robot, Sensory cue, ComputingMilieux_MISCELLANEOUS, media_common
Abstract: Robust multi-person tracking with robots opens the door to analysing engagement and social signals in real-world environments. Multi-person scenarios are charaterised by (i) a time-varying number of people, (ii) intermittent auditory (\eg speech turns) and visual cues (\eg person appearing/disappearing) and (iii) impact of the robot actions in perception. The various sensors (cameras and microphones) available for perception, provide a rich flow of information of intermittent and complementary nature. How to jointly exploit these cues to tackle the multi-person tracking problem with an autonomous system has been an intense research line of the Perception Team in the past few years. In this demo we want to present our, now mature, achievements in the field, and demonstrate two robotic systems able to track multiple persons using auditory and visual cues, when they are available. We will bring the two robots and the necessary computing resources with us, as well as the required presentation materials to discuss the models, methods and tools supporting this technology with the attendants.
Published: 2019
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16. Bayesian time-domain multiple sound source localization for a stochastic machine

Author: Emmanuel Mazer, Laurent Girin, Marvin Faix, Raphael Frisch, Jacques Droulez, Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CRISSP (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Subjects: [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Signal processing, Computer science, Bayesian probability, 020206 networking & telecommunications, Statistical model, specific hardware, 02 engineering and technology, Acoustic source localization, Bayesian stochastic machine, time-domain processing, Bayesian inference, Robustness (computer science), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Multiple sound source localization, Time domain, Algorithm, ComputingMilieux_MISCELLANEOUS
Abstract: We propose a time-domain multiple sound source localization (SSL) method based on Bayesian inference. This method is specifically designed to run on the stochastic machines (SM) that we are currently developing to perform efficient low-level sensor signal processing with ultra-low power consumption. The proposed SSL method is divided into two main parts. First, a probabilistic model is run on 50 very short time frames (3. 75ms each) of multichannel recorded signals. Second, the results obtained on the different frames are fused to obtain a final localization map. Using the system in a supervised way allows to extract estimated source locations by selecting as many maxima as there are sources in the room. We explain how this method is implemented on a SM. Experiments are presented to illustrate the performance and robustness of the resulting system.
Published: 2019
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17. Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization

Author: Simon Leglaive, Laurent Girin, Radu Horaud, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: FOS: Computer and information sciences, Computer Science::Machine Learning, Sound (cs.SD), Computer science, Gaussian, Monte Carlo method, Machine Learning (stat.ML), Multichannel speech enhancement, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computer Science - Sound, Non-negative matrix factorization, Matrix decomposition, non-negative matrix factorization, Statistics::Machine Learning, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, Audio and Speech Processing (eess.AS), Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, ComputingMilieux_MISCELLANEOUS, Artificial neural network, business.industry, local Gaussian modeling, 020206 networking & telecommunications, Pattern recognition, variational autoencoders, Speech enhancement, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Sound, symbols, Artificial intelligence, Noise (video), Monte Carlo expectation-maximization, 0305 other medical science, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: In this paper we address speaker-independent multichannel speech enhancement in unknown noisy environments. Our work is based on a well-established multichannel local Gaussian modeling framework. We propose to use a neural network for modeling the speech spectro-temporal content. The parameters of this supervised model are learned using the framework of variational autoencoders. The noisy recording environment is supposed to be unknown, so the noise spectro-temporal modeling remains unsupervised and is based on non-negative matrix factorization (NMF). We develop a Monte Carlo expectation-maximization algorithm and we experimentally show that the proposed approach outperforms its NMF-based counterpart, where speech is modeled using supervised NMF., 5 pages, 2 figures, audio examples and code available online at https://team.inria.fr/perception/icassp-2019-mvae/
Published: 2019
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18. Expectation-Maximization for Speech Source Separation using Convolutive Transfer Function

Author: Radu Horaud, Laurent Girin, Xiaofei Li, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: multiple sources, FOS: Computer and information sciences, 0209 industrial biotechnology, Sound (cs.SD), microphone arrays, expectation-maximisation algorithm, Computer science, 02 engineering and technology, Transfer function, Blind signal separation, Computer Science - Sound, 020901 industrial engineering & automation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Audio and Speech Processing (eess.AS), blind source separation, under-determined speech source separation, 0202 electrical engineering, electronic engineering, information engineering, Source separation, convolution, CTF coefficients, speech processing, multichannel microphone signals, (STFT) domain, Short-time Fourier transform, widely used narrowband assumption, convolutive transfer function, Fourier transforms, Speech enhancement, convolutive mixtures, lcsh:QA76.75-76.765, Frequency domain, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], time-domain signals, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Algorithm, Information Systems, Electrical Engineering and Systems Science - Audio and Speech Processing, reverberation, Computer Networks and Communications, Microphone, short-time Fourier, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, FOS: Electrical engineering, electronic engineering, information engineering, lcsh:Computer software, STFT domain, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], lcsh:P98-98.5, STFT coefficients, Speech processing, frequency-domain analysis, Human-Computer Interaction, microphones, mixing filters, Computer Science::Sound, source separation, room filters, transfer functions, speech enhancement, lcsh:Computational linguistics. Natural language processing
Abstract: International audience; This paper addresses the problem of under-determinded speech source separation from multichannel microphone singals, i.e. the convolutive mixtures of multiple sources. The time-domain signals are first transformed to the short-time Fourier transform (STFT) domain. To represent the room filters in the STFT domain, instead of the widely-used narrowband assumption, we propose to use a more accurate model, i.e. the convolutive transfer function (CTF). At each frequency band, the CTF coefficients of the mixing filters and the STFT coefficients of the sources are jointly estimated by maximizing the likelihood of the microphone signals, which is resolved by an Expectation-Maximization (EM) algorithm. Experiments show that the proposed method provides very satisfactory performance under highly reverberant environments
Published: 2019
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19. Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments

Author: Xavier Alameda-Pineda, Radu Horaud, Laurent Girin, Yutong Ban, Xiaofei Li, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: FOS: Computer and information sciences, Bayesian variational inference, Sound (cs.SD), Reverberation, multiple target tracking, Computer science, Speaker tracking, 02 engineering and technology, Tracking (particle physics), Computer Science - Sound, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Audio and Speech Processing (eess.AS), reverberant environments, Expectation–maximization algorithm, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, multiple moving speakers, Electrical and Electronic Engineering, Online algorithm, 020206 networking & telecommunications, Acoustic source localization, Solver, online tracking, expectation-maximization, Feature (computer vision), Computer Science::Sound, Signal Processing, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], speaker tracking, sound-source localization, Algorithm, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: We address the problem of online localization and tracking of multiple moving speakers in reverberant environments. The paper has the following contributions. We use the direct-path relative transfer function (DP-RTF), an inter-channel feature that encodes acoustic information robust against reverberation, and we propose an online algorithm well suited for estimating DP-RTFs associated with moving audio sources. Another crucial ingredient of the proposed method is its ability to properly assign DP-RTFs to audio-source directions. Towards this goal, we adopt a maximum-likelihood formulation and we propose to use an exponentiated gradient (EG) to efficiently update source-direction estimates starting from their currently available values. The problem of multiple speaker tracking is computationally intractable because the number of possible associations between observed source directions and physical speakers grows exponentially with time. We adopt a Bayesian framework and we propose a variational approximation of the posterior filtering distribution associated with multiple speaker tracking, as well as an efficient variational expectation-maximization (VEM) solver. The proposed online localization and tracking method is thoroughly evaluated using two datasets that contain recordings performed in real environments., IEEE Journal of Selected Topics in Signal Processing, 2019
Published: 2019
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20. Multichannel Speech Separation and Enhancement Using the Convolutive Transfer Function

Author: Laurent Girin, Xiaofei Li, Sharon Gannot, Radu Horaud, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Bar-Ilan University [Israël], and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Noise power, Acoustics and Ultrasonics, Computational complexity theory, Computer science, Microphone, Inverse, 02 engineering and technology, Transfer function, Computer Science - Sound, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, Lasso (statistics), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Electrical and Electronic Engineering, MINT, 020206 networking & telecommunications, convolutive transfer function, Computational Mathematics, Noise, Fourier transform, Index Terms-Audio source separation, short-time Fourier transform, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], symbols, speech enhancement, 0305 other medical science, Algorithm, Lasso optimization, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: This paper addresses the problem of speech separation and enhancement from multichannel convolutive and noisy mixtures, \emph{assuming known mixing filters}. We propose to perform the speech separation and enhancement task in the short-time Fourier transform domain, using the convolutive transfer function (CTF) approximation. Compared to time-domain filters, CTF has much less taps, consequently it has less near-common zeros among channels and less computational complexity. The work proposes three speech-source recovery methods, namely: i) the multichannel inverse filtering method, i.e. the multiple input/output inverse theorem (MINT), is exploited in the CTF domain, and for the multi-source case, ii) a beamforming-like multichannel inverse filtering method applying single source MINT and using power minimization, which is suitable whenever the source CTFs are not all known, and iii) a constrained Lasso method, where the sources are recovered by minimizing the $\ell_1$-norm to impose their spectral sparsity, with the constraint that the $\ell_2$-norm fitting cost, between the microphone signals and the mixing model involving the unknown source signals, is less than a tolerance. The noise can be reduced by setting a tolerance onto the noise power. Experiments under various acoustic conditions are carried out to evaluate the three proposed methods. The comparison between them as well as with the baseline methods is presented., Comment: Submitted to IEEE/ACM Transactions on Audio, Speech and Language Processing
Published: 2019
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21. Assessing the Performances of different Neural Network Architectures for the Detection of Screams and Shouts in Public Transportation

Author: Yun Wang, Pierre Laffitte, Laurent Girin, David Sodoyer, Laboratoire Électronique Ondes et Signaux pour les Transports (IFSTTAR/COSYS/LEOST), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Lille Nord de France, Department of Computer Science - University of Pittsburgh, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), and Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Subjects: RESEAU NEURONAL, 0209 industrial biotechnology, TRANSPORT EN COMMUN, Computer science, ACOUSTIC EVENT DETECTION, Poison control, INTELLIGENCE ARTIFICIELLE, 02 engineering and technology, NEURAL NETWORKS, Suicide prevention, Occupational safety and health, CLASSIFICATION, Task (project management), SYSTEME DE TRANSPORT INTELLIGENT, TRAITEMENT DU SIGNAL, 020901 industrial engineering & automation, Artificial Intelligence, Human–computer interaction, 11. Sustainability, SURVEILLANCE, 0202 electrical engineering, electronic engineering, information engineering, Relevance (information retrieval), DETECTION D'INCIDENT, TRAITEMENT DES IMAGES, SECURITE, Intelligent transportation system, Audio signal, Artificial neural network, AUDIO SURVEILLANCE, business.industry, General Engineering, RESEAU DE NEURONES, SON, Computer Science Applications, TRANSPORTATION, Public transport, ACOUSTIQUE, 020201 artificial intelligence & image processing, Train, DETECTION D'EVENEMENT SONORE, Noise (video), AMBIANCE SONORE, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, VIDEO
Abstract: International audience; As intelligent transportation systems are becoming more and more prevalent, the relevance of automatic surveillance systems grows larger. While such systems rely heavily on video signals, other types of signals can be used as well to monitor the security of passengers. The present article proposes an audio-based intelligent system for surveillance in public transportation, investigating the use of some state-of-the-art artificial intelligence methods for the automatic detection of screams and shouts. We present test results produced on a database of sounds occurring in subway trains in real working conditions, by classifying sounds into screams, shouts and other categories using different Neural Network architectures. The relevance of these architectures in the analysis of audio signals is analyzed. We report encouraging results, given the difficulty of the task, especially when a high level of surrounding noise is present.; Au jour où les systèmes de transport intelligents devenant de plus en plus répandus, l'intérêt des systèmes de surveillance automatique augmente. Bien que ces systèmes reposent principalement sur les signaux vidéo, d'autres types de signaux peuvent également être utilisés pour surveiller la sécurité des passagers. Le présent article propose un système automatique de reconnaissance de motifs sonores pour la surveillance à l'intérieur des transports publics, en étudiant l'utilisation de méthodes d'intelligence artificielle de pointe pour la détection automatique des cris. Nous présentons des résultats testés sur une base de données de sons enregistrés au sein même d'une rame de métro dans des conditions de travail réelles, en classant les sons en cris et en d'autres catégories en utilisant différentes architectures de réseaux de neurones. Les résultats obtenus sont encourageants, compte tenu de la difficulté de la tâche, en particulier lorsque le niveau de bruit environnant est élevé.
Published: 2019
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22. Multichannel Online Dereverberation based on Spectral Magnitude Inverse Filtering

Author: Laurent Girin, Radu Horaud, Xiaofei Li, Sharon Gannot, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Bar-Ilan University [Israël], and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Reverberation, Acoustics and Ultrasonics, Computer science, Transfer function, Computer Science - Sound, Convolution, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Electrical and Electronic Engineering, Impulse response, Short-time Fourier transform, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Speech enhancement, Computational Mathematics, Fourier transform, Computer Science::Sound, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], symbols, 0305 other medical science, Gradient descent, Algorithm, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: International audience; This paper addresses the problem of multichannel online dereverberation. The proposed method is carried out in the short-time Fourier transform (STFT) domain, and for each frequency band independently. In the STFT domain, the time-domain room impulse response is approximately represented by the convolutive transfer function (CTF). The multichannel CTFs are adaptively identified based on the cross-relation method, and using the recursive least square criterion. Instead of the complex-valued CTF convolution model, we use a nonnegative convolution model between the STFT magnitude of the source signal and the CTF magnitude, which is just a coarse approximation of the former model, but is shown to be more robust against the CTF perturbations. Based on this nonnegative model, we propose an online STFT magnitude inverse filtering method. The inverse filters of the CTF magnitude are formulated based on the multiple-input/output inverse theorem (MINT), and adaptively estimated based on the gradient descent criterion. Finally, the inverse filtering is applied to the STFT magnitude of the microphone signals, obtaining an estimate of the STFT magnitude of the source signal. Experiments regarding both speech enhancement and automatic speech recognition are conducted, which demonstrate that the proposed method can effectively suppress reverberation, even for the difficult case of a moving speaker.
Published: 2018
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23. How predictive can be predictions in the neurocognitive processing of auditory and audiovisual speech? A deep learning study

Author: E. Tatulli, Laurent Girin, Jean-Luc Schwartz, and Thomas Hueber
Subjects: Artificial neural network, Sensory processing, business.industry, Computer science, Speech recognition, medicine.medical_treatment, Deep learning, Sensory system, Stimulus (physiology), 030507 speech-language pathology & audiology, 03 medical and health sciences, 0302 clinical medicine, medicine, Artificial intelligence, Audiovisual speech, 0305 other medical science, business, Neurocognitive, 030217 neurology & neurosurgery
Abstract: Sensory processing is increasingly conceived in a predictive framework in which neurons would constantly process the error signal resulting from the comparison of expected and observed stimuli. Surprisingly, few data exist on the amount of predictions that can be computed in real sensory scenes. Here, we focus on the sensory processing of auditory and audiovisual speech. We propose a set of computational models based on artificial neural networks (mixing deep feed-forward and convolutional networks) which are trained to predict future audio observations from 25 ms to 250 ms past audio or audiovisual observations (i.e. including lip movements). Experiments are conducted on the multispeaker NTCD-TIMIT audiovisual speech database. Predictions are efficient in a short temporal range (25-50 ms), predicting 40 to 60 % of the variance of the incoming stimulus, which could result in potentially saving up to 2/3 of the processing power. Then they quickly decrease to vanish after 100 ms. Adding information on the lips slightly improves predictions, with a 5 to 10 % increase in explained variance.Interestingly the visual gain vanishes more slowly, and the gain is maximum for a delay of 75 ms between image and predicted sound.
Published: 2018
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24. A variance modeling framework based on variational autoencoders for speech enhancement

Author: Laurent Girin, Radu Horaud, Simon Leglaive, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: FOS: Computer and information sciences, Computer Science::Machine Learning, Sound (cs.SD), Computer Science - Machine Learning, Computer science, Generalization, Audio source separation, Machine Learning (stat.ML), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computer Science - Sound, Machine Learning (cs.LG), Matrix decomposition, Non-negative matrix factorization, non-negative matrix factorization, 030507 speech-language pathology & audiology, 03 medical and health sciences, Statistics::Machine Learning, Audio and Speech Processing (eess.AS), Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Source separation, Artificial neural network, business.industry, Deep learning, 020206 networking & telecommunications, Pattern recognition, Autoencoder, variational autoencoders, Speech enhancement, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Sound, speech enhancement, Artificial intelligence, Monte Carlo expectation-maximization, 0305 other medical science, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explore the use of neural networks as an alternative to a popular speech variance model based on supervised non-negative matrix factorization (NMF). More precisely, we use a variational autoencoder as a speaker-independent supervised generative speech model, highlighting the conceptual similarities that this approach shares with its NMF-based counterpart. In order to be free of generalization issues regarding the noisy recording environments, we follow the approach of having a supervised model only for the target speech signal, the noise model being based on unsupervised NMF. We develop a Monte Carlo expectation-maximization algorithm for inferring the latent variables in the variational autoencoder and estimating the unsupervised model parameters. Experiments show that the proposed method outperforms a semi-supervised NMF baseline and a state-of-the-art fully supervised deep learning approach., 6 pages, 3 figures
Published: 2018
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25. Multichannel Identification and Nonnegative Equalization for Dereverberation and Noise Reduction based on Convolutive Transfer Function

Author: Laurent Girin, Radu Horaud, Sharon Gannot, Xiaofei Li, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Bar-Ilan University [Israël], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: Frequency response, Noise power, Acoustics and Ultrasonics, Computer science, Noise reduction, 02 engineering and technology, Transfer function, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Source separation, Electrical and Electronic Engineering, Impulse response, lasso optimization, Short-time Fourier transform, audio source separation, 020206 networking & telecommunications, convolutive transfer function, Speech enhancement, Computational Mathematics, Computer Science::Sound, source separation, short-time Fourier transform, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], speech enhancement, 0305 other medical science, Algorithm
Abstract: International audience; This paper addresses the problems of blind multichannel identification and equalization for joint speech dereverberation and noise reduction. The time-domain cross-relation method is hardly applicable for blind room impulse response identification due to the near-common zeros of the long impulse responses. We extend the cross-relation method to the short-time Fourier transform (STFT) domain, in which the time-domain impulse response is approximately represented by the convolutive transfer function (CTF) with much less coefficients. For the oversampled STFT, CTFs suffer from the common zeros caused by the non-flat frequency response of the STFT window. To overcome this, we propose to identify CTFs using the STFT framework with oversampled signals and critically sampled CTFs, which is a good trade-off between the frequency aliasing of the signals and the common zeros problem of CTFs. The identified complex-valued CTFs are not accurate enough for multichannel equalization due to the frequency aliasing of the CTFs. Thence, we only use the CTF magnitudes, which leads to a nonnegative multichannel equalization method based on a nonnegative convolution model between the STFT magnitude of the source signal and the CTF magnitude. Compared with the complex-valued convolution model, this nonnegative convolution model is shown to be more robust against the CTF perturbations. To recover the STFT magnitude of the source signal and to reduce the additive noise, the L2-norm fitting error between the STFT magnitude of the microphone signals and the nonnegative convolution is constrained to be less than a noise power related tolerance. Meanwhile, the L1-norm of the STFT magnitude of the source signal is minimized to impose the sparsity.
Published: 2018
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26. Multisource MINT Using the Convolutive Transfer Function

Author: Laurent Girin, Radu Horaud, Xiaofei Li, Sharon Gannot, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Bar-Ilan University [Israël], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: Frequency response, Computer science, Short-time Fourier transform, Filter (signal processing), Impulse (physics), Transfer function, Convolution, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, Fourier transform, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Aliasing, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], symbols, Source separation, Time domain, 0305 other medical science, Algorithm
Abstract: International audience; The multichannel inverse filtering method, i.e. multiple in-put/output inverse theorem (MINT), is widely used. However, it is usually performed in the time domain, and based on the long room impulse responses, thus it has a high computational complexity and a large number of near-common zeros. In this paper, we propose to perform MINT in the short-time Fourier transform (STFT) domain, in which the time-domain filter is approximated by the convolutive transfer function. The oversampled STFT is used to avoid frequency aliasing, which however leads to a common zero region in the subband frequency response due to the frequency response of the STFT window. A new inverse filtering target function concerning the STFT window is proposed to overcome this problem. In addition, unlike most studies using MINT for single source dereverberation, the multisource MINT is proposed for both source separation and dereverberation.
Published: 2018
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27. Accounting for Room Acoustics in Audio-Visual Multi-Speaker Tracking

Author: Laurent Girin, Xiaofei Li, Radu Horaud, Yutong Ban, Xavier Alameda-Pineda, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: Reverberation, business.industry, Computer science, audio-visual fusion, multi-speaker tracking, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 02 engineering and technology, Acoustic source localization, Room acoustics, Visual field, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Robustness (computer science), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, sound-source localization, 0305 other medical science, business, dereverberation
Abstract: International audience; Multiple-speaker tracking is a crucial task for many applications. In real-world scenarios, exploiting the complementarity between auditory and visual data enables to track people outside the visual field of view. However, practical methods must be robust to changes in acoustic conditions, e.g. reverberation. We investigate how to combine state-of-the-art audio-source localization techniques with Bayesian multi-person tracking. Our experiments demonstrate that the performance of the proposed system is not affected by changes in the acoustic environment.
Published: 2018
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28. A Bayesian stochastic machine for sound source localization

Author: Laurent Girin, Raphaël Laurent, Pierre Bessière, Laurent Fesquet, Raphael Frisch, Emmanuel Mazer, Augustin Lux, Jacques Droulez, Marvin Faix, Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interaction située avec les objets et environnements intelligents (PERVASIVE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Probayes [Montbonnot], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interpretation and Modelling of Images and Videos (PERCEPTION ), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), CRISSP (GIPSA-CRISSP), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-GIPSA Pôle Parole et Cognition (GIPSA-PPC), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Interpretation and Modelling of Images and Videos (PERCEPTION), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), and ANR-11-LABX-0025-01,PERSYVAL-lab,Systèmes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)
Subjects: [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Stochastic computing, Computer science, Stochastic process, Posterior probability, Bayesian probability, 02 engineering and technology, Acoustic source localization, Bayesian inference, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Probability distribution, 020201 artificial intelligence & image processing, 0305 other medical science, Likelihood function, Algorithm
Abstract: International audience; Compared to conventional processors, stochastic computing architectures have strong potential to speed up computation time and to reduce power consumption. We present such an architecture, called Bayesian Machine (BM), dedicated to solving Bayesian inference problems. Given a set of noisy signals provided by low-level sensors, a BM estimates the posterior probability distribution of an unknown target information. In the present study, a BM is used to solve a sound source localization (SSL) problem: the BM computes the probability distribution of the position of a sound source given acoustic signals captured by a set of microphones. Assuming free field wave propagation (no reverberations), we express the SSL problem as the maximization of a likelihood function fed with audio features provided by the time-frequency (TF) analysis of the captured audio waves. The proposed BM uses bitwise parallel sampling to fuse the resulting multi-channel information. As the number of channels to fuse is large, the standard BM architecture encounters the so-called " time dilution problem " (long delays are necessary to obtain valid samples). We tackle this problem by using max-normalization of the distributions combined with a periodic re-sampling of the bit streams after processing a reasonably small subset of evidences. Finally, we compare the localization performance of the proposed machine with the results obtained using a standard version of the machine. The re-sampling leads to an impressive acceleration factor of 10³ in the computation.
Published: 2017

29. Exploiting the Complementarity of Audio and Visual Data in Multi-Speaker Tracking

Author: Laurent Girin, Yutong Ban, Xavier Alameda-Pineda, Radu Horaud, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: Computer science, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Context (language use), 02 engineering and technology, Kalman filter, Missing data, Visualization, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Complementarity (molecular biology), Outlier, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Noise (video), Artificial intelligence, 0305 other medical science, Representation (mathematics), business
Abstract: International audience; Multi-speaker tracking is a central problem in human-robot interaction. In this context, exploiting auditory and visual information is gratifying and challenging at the same time. Gratifying because the complementary nature of auditory and visual information allows us to be more robust against noise and outliers than unimodal approaches. Challenging because how to properly fuse auditory and visual information for multi-speaker tracking is far from being a solved problem. In this paper we propose a probabilistic generative model that tracks multiple speakers by jointly exploiting auditory and visual features in their own representation spaces. Importantly, the method is robust to missing data and is therefore able to track even when observations from one of the modalities are absent. Quantitative and qualitative results on the AVDIAR dataset are reported.
Published: 2017
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30. Exploiting the Intermittency of Speech for Joint Separation and Diarization

Author: Laurent Girin, Dionyssos Kounades-Bastian, Xavier Alameda-Pineda, Radu Horaud, Sharon Gannot, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Bar-Ilan University [Israël], and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: Engineering, spatial covariance matrix, Speech recognition, media_common.quotation_subject, Audio source separation, 02 engineering and technology, law.invention, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], law, Intermittency, 0202 electrical engineering, electronic engineering, information engineering, Source separation, Conversation, Hidden Markov model, media_common, business.industry, Statistical model, Speaker diarisation, EM, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 020201 artificial intelligence & image processing, speaker diarization, State (computer science), 0305 other medical science, business, Joint (audio engineering)
Abstract: International audience; Natural conversations are spontaneous exchanges involving two or more people speaking in an intermittent manner. Therefore one expects such conversation to have intervals where some of the speakers are silent. Yet, most (multichannel) audio source separation (MASS) methods consider the sound sources to be continuously emitting on the total duration of the processed mixture. In this paper we propose a probabilistic model for MASS where the sources may have pauses. The activity of the sources is modeled as a hidden state, the diarization state, enabling us to activate/de-activate the sound sources at time frame resolution. We plug the diarization model within the spatial covariance matrix model proposed for MASS, and obtain an improvement in performance over the state of the art when separating mixtures with intermittent speakers.
Published: 2017
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31. An EM Algorithm for Audio Source Separation Based on the Convolutive Transfer Function

Author: Laurent Girin, Xiaofei Li, Radu Horaud, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: Speech recognition, Audio source separation, Multiplicative function, 020206 networking & telecommunications, 02 engineering and technology, Transfer function, convolutive transfer function, Convolution, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, Fourier transform, Mixing (mathematics), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Computer Science::Sound, Expectation–maximization algorithm, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Source separation, symbols, 0305 other medical science, Hidden Markov model, EM algorithm, Algorithm, Mathematics
Abstract: International audience; This paper addresses the problem of audio source separation from (possibly under-determined) multichannel convolutive mixtures. We propose a separation method based on the convolutive transfer function (CTF) in the short-time Fourier transform domain. For strongly reverberant signals, the CTF is a much more appropriate model than the widely-used multiplicative transfer function approximation. An Expectation-Maximization (EM) algorithm is proposed to jointly estimate the model parameters, including the CTF coefficients of the mixing filters, and infer the sources. Experiments show that the proposed method provides very satisfactory performance on highly reverberant speech mixtures.
Published: 2017
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32. Multiple-Speaker Localization Based on Direct-Path Features and Likelihood Maximization with Spatial Sparsity Regularization

Author: Laurent Girin, Xiaofei Li, Sharon Gannot, Radu Horaud, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Bar-Ilan University [Israël], and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: FOS: Computer and information sciences, Reverberation, Sound (cs.SD), candidate-based GMM, Acoustics and Ultrasonics, Computer science, 02 engineering and technology, direct-path RTF, Transfer function, Computer Science - Sound, Multiple-speaker localization, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, entropy penalty, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Entropy (information theory), Electrical and Electronic Engineering, business.industry, Small number, 020206 networking & telecommunications, Pattern recognition, Mixture model, Grid, Computational Mathematics, Fourier transform, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], symbols, Artificial intelligence, 0305 other medical science, business, Binaural recording
Abstract: This paper addresses the problem of multiple-speaker localization in noisy and reverberant environments, using binaural recordings of an acoustic scene. A Gaussian mixture model (GMM) is adopted, whose components correspond to all the possible candidate source locations defined on a grid. After optimizing the GMM-based objective function, given an observed set of binaural features, both the number of sources and their locations are estimated by selecting the GMM components with the largest priors. This is achieved by enforcing a sparse solution, thus favoring a small number of speakers with respect to the large number of initial candidate source locations. An entropy-based penalty term is added to the likelihood, thus imposing sparsity over the set of GMM priors. In addition, the direct-path relative transfer function (DP-RTF) is used to build robust binaural features. The DP-RTF, recently proposed for single-source localization, was shown to be robust to reverberations, since it encodes inter-channel information corresponding to the direct-path of sound propagation. In this paper, we extend the DP-RTF estimation to the case of multiple sources. In the short-time Fourier transform domain, a consistency test is proposed to check whether a set of consecutive frames is associated to the same source or not. Reliable DP-RTF features are selected from the frames that pass the consistency test to be used for source localization. Experiments carried out using both simulation data and real data gathered with a robotic head confirm the efficiency of the proposed multi-source localization method., Comment: 16 pages, 4 figures, 4 tables
Published: 2017
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33. Automatic animation of an articulatory tongue model from ultrasound images of the vocal tract

Author: Laurent Girin, Pierre Badin, Diandra Fabre, Thomas Hueber, Xavier Alameda-Pineda, GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Department of Information Engineering and Computer Science (University of Trento ) (DISI), and University of Trento [Trento]
Subjects: Linguistics and Language, Speech production, Computer science, Speech recognition, Context (language use), Biofeedback, 01 natural sciences, Language and Linguistics, Session (web analytics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0103 physical sciences, Gaussian mixture regression, Adaptation, Articulatory talking head, 010301 acoustics, Reference model, Communication, Process (computing), [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], Statistical model, Animation, Computer Science Applications, Modeling and Simulation, Computer Vision and Pattern Recognition, 0305 other medical science, Ultrasound imaging, Software, Vocal tract
Abstract: International audience; Visual biofeedback is the process of gaining awareness of physiological functions through the display of visual information. As speech is concerned, visual biofeedback usually consists in showing a speaker his/her own articulatory movements, which has proven useful in applications such as speech therapy or second language learning. This article presents a novel method for automatically animating an articulatory tongue model from ultrasound images. Integrating this model into a virtual talking head enables to overcome the limitations of displaying raw ultrasound images, and provides a more complete and user-friendly feedback by showing not only the tongue, but also the palate, teeth, pharynx, etc. Altogether, these cues are expected to lead to an easier understanding of the tongue movements. Our approach is based on a probabilistic model which converts raw ultrasound images of the vocal tract into control parameters of the articulatory tongue model. We investigated several mapping techniques such as the Gaussian Mixture Regression (GMR), and in particular the Cascaded Gaussian Mixture Regression (C-GMR) techniques, recently proposed in the context of acoustic-articulatory inversion. Both techniques are evaluated on a multispeaker database. The C-GMR consists in the adaptation of a GMR reference model, trained with a large dataset of multimodal articulatory data from a reference speaker, to a new source speaker using a small set of adaptation data recorded during a preliminary enrollment session (system calibration). By using prior information from the reference model, the C-GMR approach is able (i) to maintain good mapping performance while minimizing the amount of adaptation data (and thus limiting the duration of the enrollment session), and (ii) to generalize to articulatory configurations not seen during enrollment better than the GMR approach. As a result, the C-GMR appears to be a good mapping technique for a practical system of visual biofeedback.
Published: 2017
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34. An EM Algorithm for Joint Source Separation and Diarisation of Multichannel Convolutive Speech Mixtures

Author: Laurent Girin, Radu Horaud, Dionyssos Kounades-Bastian, Sharon Gannot, Xavier Alameda-Pineda, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), University of Trento [Trento], Bar-Ilan University [Israël], European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: Computer science, Speech recognition, Audio source separation, 02 engineering and technology, Non-negative matrix factorization, Matrix decomposition, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Expectation–maximization algorithm, 0202 electrical engineering, electronic engineering, information engineering, Source separation, Hidden Markov model, business.industry, speaker diarisation, 020206 networking & telecommunications, Statistical model, Pattern recognition, Speaker diarisation, local Gaussian model, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], symbols, Artificial intelligence, 0305 other medical science, business, Gaussian network model
Abstract: International audience; We present a probabilistic model for joint source separation and diarisation of multichannel convolutive speech mixtures. We build upon the framework of local Gaussian model (LGM) with non-negative matrix factorization (NMF). The diarisa-tion is introduced as a temporal labeling of each source in the mix as active or inactive at the short-term frame level. We devise an EM algorithm in which the source separation process is aided by the diarisation state, since the latter indicates the sources actually present in the mixture. The diarisation state is tracked with a Hidden Markov Model (HMM) with emission probabilities calculated from the estimated source signals. The proposed EM has separation performance comparable with a state-of-the-art LGM NMF method, while out-performing a state-of-the-art speaker diarisation pipeline.
Published: 2017
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35. Audio Source Separation Based on Convolutive Transfer Function and Frequency-Domain Lasso Optimization

Author: Radu Horaud, Xiaofei Li, Laurent Girin, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Speech recognition, Multiplicative function, 02 engineering and technology, Transfer function, convolutive transfer function, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, Fourier transform, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Frequency domain, Norm (mathematics), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, Source separation, symbols, 020201 artificial intelligence & image processing, 0305 other medical science, Algorithm, $l_1$-norm regularization, Mathematics, Separation procedure
Abstract: International audience; This paper addresses the problem of under-determined convolutive audio source separation in a semi-oracle configuration where the mixing filters are assumed to be known. We propose a separation procedure based on the convolutive transfer function (CTF), which is a more appropriate model for strongly reverberant signals than the widely-used multi-plicative transfer function approximation. In the short-time Fourier transform domain, source signals are estimated by minimizing the mixture fitting cost using Lasso optimization, with a $l_1$-norm regularization to exploit the spectral sparsity of source signals. Experiments show that the proposed method achieves satisfactory performance on highly reverberant speech mixtures, with a much lower computational cost compared to time-domain dual techniques.
Published: 2017
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36. Estimation of the Direct-Path Relative Transfer Function for Supervised Sound-Source Localization

Author: Radu Horaud, Laurent Girin, Sharon Gannot, Xiaofei Li, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Faculty of Engineering [Israel], Bar-Ilan University [Israël], European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014), and European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Acoustics and Ultrasonics, Microphone, Computer science, inter-frame spectral subtraction, 02 engineering and technology, Transfer function, Computer Science - Sound, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), direct-path relative transfer function, Electrical and Electronic Engineering, Impulse response, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Short-time Fourier transform, 020206 networking & telecommunications, Acoustic source localization, Computational Mathematics, Noise, Fourier transform, Computer Science::Sound, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], symbols, 0305 other medical science, Algorithm, Binaural recording, binaural source localization
Abstract: This paper addresses the problem of binaural localization of a single speech source in noisy and reverberant environments. For a given binaural microphone setup, the binaural response corresponding to the direct-path propagation of a single source is a function of the source direction. In practice, this response is contaminated by noise and reverberations. The direct-path relative transfer function (DP-RTF) is defined as the ratio between the direct-path acoustic transfer function of the two channels. We propose a method to estimate the DP-RTF from the noisy and reverberant microphone signals in the short-time Fourier transform domain. First, the convolutive transfer function approximation is adopted to accurately represent the impulse response of the sensors in the STFT domain. Second, the DP-RTF is estimated by using the auto- and cross-power spectral densities at each frequency and over multiple frames. In the presence of stationary noise, an inter-frame spectral subtraction algorithm is proposed, which enables to achieve the estimation of noise-free auto- and cross-power spectral densities. Finally, the estimated DP-RTFs are concatenated across frequencies and used as a feature vector for the localization of speech source. Experiments with both simulated and real data show that the proposed localization method performs well, even under severe adverse acoustic conditions, and outperforms state-of-the-art localization methods under most of the acoustic conditions., 15 pages, 7 figures, 5 tables
Published: 2016
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37. Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces

Author: Laurent Girin, Thomas Hueber, Blaise Yvert, Christophe Savariaux, Florent Bocquelet, GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), BrainTech Laboratory [CHU Grenoble Alpes - Inserm U1205] (Brain Tech Lab ), CHU Grenoble-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and GIPSA-Services (GIPSA-Services)
Subjects: 0301 basic medicine, Sound Spectrography, Computer science, Audio Signal Processing, Speech recognition, Speech coding, Social Sciences, Speech synthesis, Intelligibility (communication), computer.software_genre, Communication Aids for Disabled, 0302 clinical medicine, Speech Production Measurement, Medicine and Health Sciences, Audio Equipment, lcsh:QH301-705.5, Speech Acoustics, Ecology, Physics, Acoustic model, Signal Filtering, Computational Theory and Mathematics, Brain-Computer Interfaces, Modeling and Simulation, Physical Sciences, Engineering and Technology, Anatomy, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Research Article, Equipment, 03 medical and health sciences, Cellular and Molecular Neuroscience, Tongue, Computer Systems, Phonetics, Acoustic Signals, Genetics, Humans, Speech, Vowels, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Mouth, Voice activity detection, Speech Intelligibility, Biology and Life Sciences, Biofeedback, Psychology, Linguistics, Acoustics, Speech processing, 030104 developmental biology, lcsh:Biology (General), Speech Signal Processing, Signal Processing, Neural Networks, Computer, Digestive System, computer, 030217 neurology & neurosurgery
Abstract: Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech. The articulatory-to-acoustic mapping is performed using a deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded on a reference speaker synchronously with the produced speech signal. This DNN is then used in both offline and online modes to map the position of sensors glued on different speech articulators into acoustic parameters that are further converted into an audio signal using a vocoder. In offline mode, highly intelligible speech could be obtained as assessed by perceptual evaluation performed by 12 listeners. Then, to anticipate future BCI applications, we further assessed the real-time control of the synthesizer by both the reference speaker and new speakers, in a closed-loop paradigm using EMA data recorded in real time. A short calibration period was used to compensate for differences in sensor positions and articulatory differences between new speakers and the reference speaker. We found that real-time synthesis of vowels and consonants was possible with good intelligibility. In conclusion, these results open to future speech BCI applications using such articulatory-based speech synthesizer., Author Summary Speech Brain-Computer-Interfaces may restore communication for people with severe paralysis by decoding cortical speech activity to control in real time a speech synthesizer. Recent data indicate that the speech motor cortex houses a detailed topographical representation of the different speech articulators of the vocal tract. Its activity could thus be used in a speech BCI paradigm to control an articulatory-based speech synthesizer. To date, there has been no report of an articulatory-based speech synthesizer producing fully intelligible speech, as well as no evidence that such device could be controlled in real time by a naive subject. Here, we present an articulatory-based speech synthesizer and show that it could be controlled in real time by several subjects articulating silently to produce intelligible speech.
Published: 2016
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38. Voice Activity Detection Based on Statistical Likelihood Ratio With Adaptive Thresholding

Author: Laurent Girin, Xiaofei Li, Radu Horaud, Sharon Gannot, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Bar-Ilan University [Israël], and European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014)
Subjects: adaptive threshold, 02 engineering and technology, Noise (electronics), Upper and lower bounds, 030507 speech-language pathology & audiology, 03 medical and health sciences, Signal-to-noise ratio, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics, 0202 electrical engineering, electronic engineering, information engineering, high non-speech hit rate, Mathematics, Voice activity detection, Noise measurement, business.industry, 020206 networking & telecommunications, Pattern recognition, likelihood ratio test, Thresholding, voice activity detection, Computer Science::Sound, Likelihood-ratio test, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Hit rate, Artificial intelligence, 0305 other medical science, business
Abstract: International audience; Statistical likelihood ratio test is a widely used voice activity detection (VAD) method, in which the likelihood ratio of the current temporal frame is compared with a threshold. A fixed threshold is always used, but this is not suitable for various types of noise. In this paper, an adaptive threshold is proposed as a function of the local statistics of the likelihood ratio. This threshold represents the upper bound of the likelihood ratio for the non-speech frames, whereas it remains generally lower than the likelihood ratio for the speech frames. As a result, a high non-speech hit rate can be achieved, while maintaining speech hit rate as large as possible.
Published: 2016
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39. A Variational EM Algorithm for the Separation of Time-Varying Convolutive Audio Mixtures

Author: Laurent Girin, Radu Horaud, Xavier Alameda-Pineda, Dionyssos Kounades-Bastian, Sharon Gannot, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), University of Trento [Trento], Faculty of Engineering [Israel], Bar-Ilan University [Israël], European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014), and European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Acoustics and Ultrasonics, Computer science, Audio source separation, Separation (statistics), 02 engineering and technology, Computer Science - Sound, Matrix decomposition, Moving sources, 030507 speech-language pathology & audiology, 03 medical and health sciences, Matrix (mathematics), Mixing (mathematics), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Expectation–maximization algorithm, Variational EM, 0202 electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Electrical and Electronic Engineering, Probabilistic framework, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Stochastic process, Estimator, 020206 networking & telecommunications, Computational Mathematics, Kalman smoother, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Time-varying mixing filters, 0305 other medical science, Algorithm
Abstract: This paper addresses the problem of separating audio sources from time-varying convolutive mixtures. We propose a probabilistic framework based on the local complex-Gaussian model combined with non-negative matrix factorization. The time-varying mixing filters are modeled by a continuous temporal stochastic process. We present a variational expectation-maximization (VEM) algorithm that employs a Kalman smoother to estimate the time-varying mixing matrix, and that jointly estimate the source parameters. The sound sources are then separated by Wiener filters constructed with the estimators provided by the VEM algorithm. Extensive experiments on simulated data show that the proposed method outperforms a block-wise version of a state-of-the-art baseline method., Comment: 13 pages, 4 figures, 2 tables
Published: 2016
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40. A Low Bit-Rate Speech Codec Based on a Long-Term Harmonic Plus Noise Model

Author: Laurent Girin, Faten Ben Ali, Sonia Djaziri-Larbi, Ecole Nationale d'Ingénieurs de Tunis (ENIT), Université de Tunis El Manar (UTM), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Unité Signaux et Systèmes, and Université de Tunis El Manar (UTM)-Université de Tunis El Manar (UTM)
Subjects: Computer science, Speech recognition, 05 social sciences, Speech coding, General Engineering, Vector quantization, Intelligibility (communication), 03 medical and health sciences, Noise, 0302 clinical medicine, Adaptive Multi-Rate audio codec, Codec2, 0502 economics and business, Harmonic, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 050203 business & management, 030217 neurology & neurosurgery, Music, ComputingMilieux_MISCELLANEOUS, Data compression
Abstract: International audience; The long-term harmonic plus noise model (LT-HNM) for speech shows an interesting data compression, since it exploits the smooth evolution of the time trajectories of the short-term harmonic plus noise model parameters, by applying a discrete cosine model (DCM). In this paper, we extend the LT-HNM to a complete low bit-rate speech coder. A Normalized Split Vector Quantization (NSVQ) is proposed to quantize the variable dimension LT-DCM vectors. The NSVQ is designed according to the properties of the DCM vectors obtained from a standard speech database. The obtained LT-HNM coder reaches an average bit-rate of 2.7kbps for wideband speech. The proposed coder is evaluated in terms of modeling and coding errors, bit-rate, listening quality and intelligibility. Index Terms Low bit-rate, speech coding, long term modeling, harmonic plus noise model, variable dimension vector quantization.
Published: 2016
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41. An Inverse-Gamma Source Variance Prior with Factorized Parameterization for Audio Source Separation

Author: Laurent Girin, Radu Horaud, Xavier Alameda-Pineda, Dionyssos Kounades-Bastian, Sharon Gannot, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), University of Trento [Trento], Faculty of Engineering [Israel], Bar-Ilan University [Israël], IEEE Signal Processing Society, European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014), and European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014)
Subjects: Underdetermined system, Computer science, 02 engineering and technology, Blind signal separation, Non-negative matrix factorization, PSD model, 030507 speech-language pathology & audiology, 03 medical and health sciences, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Expectation–maximization algorithm, 0202 electrical engineering, electronic engineering, information engineering, Source separation, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Audio signal, business.industry, Estimation theory, Spectral density, audio source separation, 020206 networking & telecommunications, Statistical model, Pattern recognition, local Gaussian model, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Artificial intelligence, Audio modeling, 0305 other medical science, business, Algorithm, Scale parameter
Abstract: International audience; In this paper we present a new statistical model for the power spectral density (PSD) of an audio signal and its application to multichannel audio source separation (MASS). The source signal is modeled with the local Gaussian model (LGM) and we propose to model its variance with an inverse-Gamma distribution, whose scale parameter is factorized as a rank-1 model. We discuss the interest of this approach and evaluate it in a MASS task with underdetermined convolutive mixtures. For this aim, we derive a variational EM algorithm for parameter estimation and source inference. The proposed model shows a benefit in source separation performance compared to a state-of-the-art LGM NMF-based technique.
Published: 2016
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42. Non-Stationary Noise Power Spectral Density Estimation Based on Regional Statistics

Author: Laurent Girin, Radu Horaud, Sharon Gannot, Xiaofei Li, Interpretation and Modelling of Images and Videos (PERCEPTION ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Faculty of Engineering [Israel], Bar-Ilan University [Israël], IEEE Signal Processing Society, and European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014)
Subjects: Noise power, Computer science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, Statistics, 0202 electrical engineering, electronic engineering, information engineering, Value noise, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Noise measurement, Spectral density, regional statistics, speech presence probability, Gradient noise, Speech enhancement, Noise, Gaussian noise, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], symbols, 020201 artificial intelligence & image processing, noise PSD, 0305 other medical science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Smoothing
Abstract: International audience; Estimating the noise power spectral density (PSD) is essential for single channel speech enhancement algorithms. In this paper, we propose a noise PSD estimation approach based on regional statistics. The proposed regional statistics consist of four features representing the statistics of the past and present periodograms in a short-time period. We show that these features are efficient in characterizing the statistical difference between noise PSD and noisy speech PSD. We therefore propose to use these features for estimating the speech presence probability (SPP). The noise PSD is recursively estimated by averaging past spectral power values with a time-varying smoothing parameter controlled by the SPP. The proposed method exhibits good tracking capability for non-stationary noise, even for abruptly increasing noise level.
Published: 2016
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43. Speaker-Adaptive Acoustic-Articulatory Inversion using Cascaded Gaussian Mixture Regression

Author: Laurent Girin, Gérard Bailly, Thomas Hueber, Xavier Alameda-Pineda, GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Interpretation and Modelling of Images and Videos (PERCEPTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
Subjects: Acoustics and Ultrasonics, Computer science, speech production, Speech recognition, pronunciation training, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Expectation–maximization algorithm, Computer Science (miscellaneous), Gaussian mixture regression, Electrical and Electronic Engineering, Hidden Markov model, talking head, EM algorithm, Reference model, business.industry, speaker adaptation, Statistical model, Pattern recognition, Mixture model, Speaker recognition, Speaker diarisation, Acoustic space, Computational Mathematics, Artificial intelligence, Acoustic-articulatory inversion, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; This paper addresses the adaptation of an acoustic-articulatory model of a reference speaker to the voice of another speaker, using a limited amount of audio-only data. In the context of pronunciation training, a virtual talking head displaying the internal speech articulators (e.g., the tongue) could be automatically animated by means of such a model using only the speaker's voice. In this study, the articulatory-acoustic relationship of the reference speaker is modeled by a gaussian mixture model (GMM). To address the speaker adaptation problem, we propose a new framework called cascaded Gaussian mixture regression (C-GMR), and derive two implementations. The first one, referred to as Split-C-GMR, is a straightforward chaining of two distinct GMRs: one mapping the acoustic features of the source speaker into the acoustic space of the reference speaker, and the other estimating the articulatory trajectories with the reference model. In the second implementation, referred to as Integrated-C-GMR, the two mapping steps are tied together in a single probabilistic model. For this latter model, we present the full derivation of the exact EM training algorithm, that explicitly exploits the missing data methodology of machine learning. Other adaptation schemes based on maximum-a posteriori (MAP), maximum likelihood linear regression (MLLR) and direct cross-speaker acoustic-to-articulatory GMR are also investigated. Experiments conducted on two speakers for different amount of adaptation data show the interest of the proposed C-GMR techniques.
Published: 2015
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44. Local Relative Transfer Function for Sound Source Localization

Author: Radu Horaud, Laurent Girin, Xiaofei Li, Sharon Gannot, Interpretation and Modelling of Images and Videos (PERCEPTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Bar-Ilan University [Israël], European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
Subjects: Beamforming, Microphone array, Computer science, Acoustics, Estimator, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Acoustic source localization, Transfer function, microphone array, sound source localization, Signal-to-noise ratio, Sensor array, Noise-canceling microphone, relative transfer function
Abstract: International audience; The relative transfer function (RTF), i.e. the ratio of acoustic transfer functions between two sensors, can be used for sound source localization / beamforming based on a microphone array. The RTF is usually defined with respect to a unique reference sensor. Choosing the reference sensor may be a difficult task, especially for dynamic acoustic environment and setup. In this paper we propose to use a locally normalized RTF, in short local-RTF, as an acoustic feature to characterize the source direction. Local-RTF takes a neighbor sensor as the reference channel for a given sensor. The estimated local-RTF vector can thus avoid the bad effects of a noisy unique reference and have smaller estimation error than conventional RTF estimators. We propose two estimators for the local-RTF and concatenate the values across sensors and frequencies to form a high-dimensional vector which is utilized for source localization. Experiments with real-world signals show the interest of this approach.
Published: 2015
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45. Estimation of Relative Transfer Function in the Presence of Stationary Noise Based on Segmental Power Spectral Density Matrix Subtraction

Author: Xiaofei Li, Laurent Girin, Sharon Gannot, Radu Horaud, Interpretation and Modelling of Images and Videos (PERCEPTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Faculty of Engineering [Israel], Bar-Ilan University [Israël], European Project: 609465,EC:FP7:ICT,FP7-ICT-2013-10,EARS(2014), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Noise temperature, Noise measurement, Speech recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, microphone array, Gradient noise, Noise, Matrix (mathematics), Colors of noise, Computer Science::Sound, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Value noise, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Eigendecomposition of a matrix, stationary noise, relative transfer function, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA], Mathematics
Abstract: International audience; This paper addresses the problem of relative transfer function (RTF) estimation in the presence of stationary noise. We propose an RTF identification method based on segmental power spectral density (PSD) matrix subtraction. First multiple channel microphone signals are divided into segments corresponding to speech-plus-noise activity and noise-only. Then, the subtraction of two segmental PSD matrices leads to an almost noise-free PSD matrix by reducing the stationary noise component and preserving non-stationary speech component. This noise-free PSD matrix is used for single speaker RTF identification by eigenvalue decomposition. Experiments are performed in the context of sound source localization to evaluate the efficiency of the proposed method.
Published: 2015
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46. Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression

Author: Laurent Girin, Radu Horaud, Antoine Deleforge, Yoav Y. Schechner, Interpretation and Modelling of Images and Videos (PERCEPTION), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Department of Electrical Engineering - Technion [Haïfa] (EE-Technion), Technion - Israel Institute of Technology [Haifa], GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014), Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF)-Université Pierre Mendès France - Grenoble 2 (UPMF), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Acoustics and Ultrasonics, Computer science, Gaussian, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.7: Natural Language Processing/I.2.7.5: Speech recognition and synthesis, Machine Learning (stat.ML), Monaural, Statistics - Applications, supervised learning, Computer Science - Sound, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, Sound-source localization, Linear regression, Computer Science (miscellaneous), Source separation, Applications (stat.AP), Electrical and Electronic Engineering, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], mixture model, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.5: Sound and Music Computing, business.industry, audio-visual fusion, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Regression analysis, White noise, Multimedia (cs.MM), Computational Mathematics, Range (mathematics), binaural hearing, symbols, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.10: Vision and Scene Understanding/I.2.10.9: Video analysis, regression, Artificial intelligence, business, Binaural recording, Computer Science - Multimedia
Abstract: This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally-linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods., 15 pages, 8 figures
Published: 2015
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47. Real-time Control of a DNN-based Articulatory Synthesizer for Silent Speech Conversion: a pilot study

Author: Thomas Hueber, Christophe Savariaux, Laurent Girin, Blaise Yvert, Florent Bocquelet, BOCQUELET, Florent, GIPSA - Cognitive Robotics, Interactive Systems, & Speech Processing (GIPSA-CRISSP), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA-Services (GIPSA-Services), Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, articulatory speech synthesis, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-SD] Computer Science [cs]/Sound [cs.SD], 030507 speech-language pathology & audiology, 03 medical and health sciences, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], deep neural networks, Real-time Control System, EMA, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, 0305 other medical science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, silent speech, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; This article presents a pilot study on the real-time control of an articulatory synthesizer based on deep neural network (DNN), in the context of silent speech interface. The underlying hypothesis is that a silent speaker could benefit from real-time audio feedback to regulate his/her own production. In this study, we use 3D electromagnetic-articulography (EMA) to capture speech articulation, a DNN to convert EMA to spectral trajectories in real-time, and a standard vocoder excited by white noise for audio synthesis. As shown by recent literature on silent speech, adaptation of the articulo-acoustic modeling process is needed to account for possible inconsistencies between the initial training phase and practical usage conditions. In this study, we focus on different sensor setups across sessions (for the same speaker). Model adaptation is performed by cascading another neural network to the DNN used for articulatory-to-acoustic mapping. The intelligibility of the synthetic speech signal converted in real-time is evaluated using both objective and perceptual measurements.
Published: 2015

48. Robust Articulatory Speech Synthesis using Deep Neural Networks for BCI Applications

Author: Laurent Girin, Pierre Badin, Blaise Yvert, Thomas Hueber, Florent Bocquelet, GIPSA - Machines parlantes, Gestes oro-faciaux, Interaction Face-à-face, Communication augmentée (GIPSA-MAGIC), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Clinatec - Centre de recherche biomédicale Edmond J.Safra (SCLIN), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Badin, Pierre
Subjects: Computer science, articulatory speech synthesis, Speech recognition, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, Speech synthesis, 02 engineering and technology, Intelligibility (communication), computer.software_genre, [SHS.INFO] Humanities and Social Sciences/Library and information sciences, noise robustness, brain computer interface (BCI), EMA, deep auto-encoder, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, 050107 human factors, Brain–computer interface, dimensionality reduction, Voice activity detection, Artificial neural network, 05 social sciences, 020206 networking & telecommunications, Mixture model, deep neural networks, Deep neural networks, computer
Abstract: Brain-Computer Interfaces (BCIs) usually propose typing strategies to restore communication for paralyzed and aphasic people. A more natural way would be to use speech BCI directly controlling a speech synthesizer. Toward this goal, a prerequisite is the development a synthesizer that should i) produce intelligible speech, ii) run in real time, iii) depend on as few parameters as possible, and iv) be robust to error fluctuations on the control parameters. In this context, we describe here an articulatory-to-acoustic mapping approach based on deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded synchronously with produced speech sounds. On this corpus, the DNN-based model provided a speech synthesis quality (as assessed by automatic speech recognition and behavioral testing) comparable to a state-of-the-art Gaussian mixture model (GMM), yet showing higher robustness when noise was added to the EMA coordinates. Moreover, to envision BCI applications, this robustness was also assessed when the space covered by the 12 original articulatory parameters was reduced to 7 parameters using deep auto-encoders (DAE). Given that this method can be implemented in real time, DNN-based articulatory speech synthesis seems a good candidate for speech BCI applications. Index Terms: articulatory speech synthesis, brain computer interface (BCI), deep neural networks, deep auto-encoder, EMA, noise robustness, dimensionality reduction
Published: 2014

49. Informed Source Separation from compressed mixtures using spatial wiener filter and quantization noise estimation

Author: Laurent Girin, Antoine Liutkus, Shuhua Zhang, GIPSA - Machines parlantes, Gestes oro-faciaux, Interaction Face-à-face, Communication augmentée (GIPSA-MAGIC), Département Parole et Cognition (GIPSA-DPC), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), and ANR DReaM
Subjects: Work (thermodynamics), Computer science, Speech recognition, 02 engineering and technology, Data_CODINGANDINFORMATIONTHEORY, De- noising, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Source separation, Bitstream, AAC, Wiener Filter, Quantization (signal processing), Wiener filter, Short-time Fourier transform, Process (computing), Wiener deconvolution, 020206 networking & telecommunications, Uncompressed video, NTF, Informed Source Separation, symbols, 0305 other medical science, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; In a previous work, we proposed an Informed Source Separation sys- tem based on Wiener filtering for active listening of music from un- compressed (16-bit PCM) multichannel mix signals. In the present work, the system is improved to work with (MPEG-2 AAC) com- pressed mix signals: quantization noise is estimated from the AAC bitstream at the decoder and explicitly taken into account in the source separation process. Also a direct MDCT-to-STFT transform is used to optimize the computational efficiency of the process in the STFT domain from AAC-decoded MDCT coefficients.
Published: 2013
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50. Informed source separation through spectrogram coding and data embedding

Author: Laurent Girin, Jonathan Pinel, Antoine Liutkus, Gael Richard, Roland Badeau, Laboratoire Traitement et Communication de l'Information (LTCI), Télécom ParisTech-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Machines parlantes, Gestes oro-faciaux, Interaction Face-à-face, Communication augmentée (GIPSA-MAGIC), Département Parole et Cognition (GIPSA-DPC), Département Traitement du Signal et des Images (TSI), and Télécom ParisTech-Centre National de la Recherche Scientifique (CNRS)
Subjects: Theoretical computer science, Audio source separation, Data embedding, 02 engineering and technology, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Source separation, Electrical and Electronic Engineering, Wiener filtering, Gaussian process, Mathematics, Wiener filter, 020206 networking & telecommunications, NTF, Control and Systems Engineering, Signal Processing, symbols, Spectrogram, Embedding, Computer Vision and Pattern Recognition, 0305 other medical science, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software, Decoding methods, Linear filter, Image compression
Abstract: International audience; We address the issue of underdetermined source separation in a particular informed configuration where both the sources and the mixtures are known during a so-called encoding stage. This knowledge enables the computation of a side-information which is small enough to be inaudibly embedded into the mixtures. At the decoding stage, the sources are no longer assumed to be known, only the mixtures and the extracted side-information are processed for source separation. The proposed system models the sources as independent and locally stationary Gaussian processes (GP) and the mixing process as a linear filtering. This model allows reliable estimation of the sources through generalized Wiener filtering, provided their spectrograms are known. As these spectrograms are too large to be embedded in the mixtures, we show how they can be efficiently approximated using either Nonnegative Tensor Factorization (NTF) or image compression. A high-capacity embedding method is used by the system to inaudibly embed the separation side-information into the mixtures. This method is an application of the Quantization Index Modulation technique applied to the time-frequency coefficients of the mixtures and permits to reach embedding rates of about 250 kbps. Finally, a study of the performance of the full system is presented.
Published: 2012
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