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1. Unsupervised Speech Enhancement Using Dynamical Variational Autoencoders

Author: Xiaoyu Bie, Simon Leglaive, Xavier Alameda-Pineda, Laurent Girin, 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), CentraleSupélec, 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), 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), ANR-19-P3IA-0003, ANR-3IA MIAI, ANR-19-CE33-0008-01, ANR-JCJC ML3RI, GA #871245, EC, 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), and European Project: H2020,SPRING
Subjects: Computer Science::Machine Learning, FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), Acoustics and Ultrasonics, Computer Science - Artificial Intelligence, Noise measurement, Speech enhancement, Time series analysis, Computer Science - Sound, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning (cs.LG), Time-domain analysis, Computational Mathematics, Artificial Intelligence (cs.AI), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Computer Science::Sound, Audio and Speech Processing (eess.AS), Recording, FOS: Electrical engineering, electronic engineering, information engineering, Computer Science (miscellaneous), Training, Inference algorithms, Electrical and Electronic Engineering, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: International audience; Dynamical variational autoencoders (DVAEs) are a class of deep generative models with latent variables, dedicated to model time series of high-dimensional data. DVAEs can be considered as extensions of the variational autoencoder (VAE) that include temporal dependencies between successive observed and/or latent vectors. Previous work has shown the interest of using DVAEs over the VAE for speech spectrograms modeling. Independently, the VAE has been successfully applied to speech enhancement in noise, in an unsupervised noise-agnostic set-up that requires neither noise samples nor noisy speech samples at training time, but only requires clean speech signals. In this paper, we extend these works to DVAE-based single-channel unsupervised speech enhancement, hence exploiting both speech signals unsupervised representation learning and dynamics modeling. We propose an unsupervised speech enhancement algorithm that combines a DVAE speech prior pre-trained on clean speech signals with a noise model based on nonnegative matrix factorization, and we derive a variational expectation-maximization (VEM) algorithm to perform speech enhancement. The algorithm is presented with the most general DVAE formulation and is then applied with three specific DVAE models to illustrate the versatility of the framework. Experimental results show that the proposed DVAE-based approach outperforms its VAE-based counterpart, as well as several supervised and unsupervised noise-dependent baselines, especially when the noise type is unseen during training.
Published: 2022
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2. Les auto-encodeurs variationnels dynamiques et leur application à la modélisation de spectrogrammes de parole

Author: Laurent Girin, Xiaoyu Bie, Simon Leglaive, Thomas Hueber, and Xavier Alameda-Pineda
Published: 2022
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3. High-Resolution Speaker Counting in Reverberant Rooms Using CRNN with Ambisonics Features

Author: Pierre-Amaury Grumiaux, Laurent Girin, Srdan Kitic, and Alexandre Guerin
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Reverberation, Microphone, Computer science, Ambisonics, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, computer.software_genre, Computer Science - Sound, Speaker diarisation, Sound recording and reproduction, Noise, Recurrent neural network, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Audio signal processing, computer, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Speaker counting is the task of estimating the number of people that are simultaneously speaking in an audio recording. For several audio processing tasks such as speaker diarization, separation, localization and tracking, knowing the number of speakers at each timestep is a prerequisite, or at least it can be a strong advantage, in addition to enabling a low latency processing. For that purpose, we address the speaker counting problem with a multichannel convolutional recurrent neural network which produces an estimation at a short-term frame resolution. We trained the network to predict up to 5 concurrent speakers in a multichannel mixture, with simulated data including many different conditions in terms of source and microphone positions, reverberation, and noise. The network can predict the number of speakers with good accuracy at frame resolution., 5 pages, 1 figure
Published: 2021
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4. Dynamical Variational Autoencoders: A Comprehensive Review

Author: Xavier Alameda-Pineda, Thomas Hueber, Julien Diard, Xiaoyu Bie, Simon Leglaive, and Laurent Girin
Published: 2021
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5. Extending the Cascaded Gaussian Mixture Regression Framework for Cross-Speaker Acoustic-Articulatory Mapping

Author: Laurent Girin, Thomas Hueber, 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 University of Trento [Trento]
Subjects: 0209 industrial biotechnology, Acoustics and Ultrasonics, Speaker adaptation, Computer science, Missing data, Speech recognition, Inference, 02 engineering and technology, Gaussian mixture regression, Data modeling, 030507 speech-language pathology & audiology, 03 medical and health sciences, 020901 industrial engineering & automation, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Computer Science (miscellaneous), GMM, Electrical and Electronic Engineering, Inversion (meteorology), Statistical model, Speech processing, Mixture model, Computational Mathematics, EM, Acoustic-articulatory inversion, 0305 other medical science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: International audience; This article addresses the adaptation of an acoustic-articulatory inversion model of a reference speaker to the voice of another source speaker, using a limited amount of audio-only data. In this study, the articulatory-acoustic relationship of the reference speaker is modeled by a Gaussian mixture model and inference of articulatory data from acoustic data is made by the associated Gaussian mixture regression (GMR). To address speaker adaptation, we previously proposed a general framework called Cascaded-GMR (C-GMR) which decomposes the adaptation process into two consecutive steps: spectral conversion between source and reference speaker and acoustic-articulatory inversion of converted spectral trajectories. In particular, we proposed the Integrated C-GMR technique (IC-GMR) in which both steps are tied together in the same probabilistic model. In this article, we extend the C-GMR framework with another model called Joint-GMR (J-GMR). Contrary to the IC-GMR, this model aims at exploiting all potential acoustic-articulatory relationships, including those between the source speaker's acoustics and the reference speaker's articulation. We present the full derivation of the exact Expectation-Maximization (EM) training algorithm for the J-GMR. It exploits the missing data methodology of machine learning to deal with limited adaptation data. We provide an extensive evaluation of the J-GMR on both synthetic acoustic-articulatory data and on the multi-speaker MOCHA EMA database. We compare the J-GMR performance to other models of the C-GMR framework, notably the IC-GMR, and discuss their respective merits.
Published: 2017
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6. Speech Enhancement with Variational Autoencoders and Alpha-stable Distributions

Author: Laurent Girin, Radu Horaud, Antoine Liutkus, Umut Simsekli, 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]), Signal, Statistique et Apprentissage (S2A), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), Télécom ParisTech, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), 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]), Chaire DSAIDISThis work is supported by the ERC Advanced Grant VHIA #34, ANR-15-CE38-0003,KAMoulox,Démixage en ligne de larges archives sonores(2015), ANR-16-CE23-0014,FBIMATRIX,Méthodes distribuées et parallèles de Monte-Carlo par chaînes de Markov pour l'Inférence Bayésienne de modèles à factorisation de tenseurs(2016), European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Subjects: FOS: Computer and information sciences, Sound (cs.SD), Computer science, Gaussian, Speech recognition, Speech enhancement, Monte Carlo method, Machine Learning (stat.ML), 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Intelligibility (communication), Computer Science - Sound, Matrix decomposition, 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, Context model, Noise measurement, business.industry, Deep learning, 020206 networking & telecommunications, Computer Science::Sound, symbols, Monte Carlo expectation-maximization, Artificial intelligence, 0305 other medical science, business, Variational autoencoders, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Alpha-stable distribution, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders. The noise model remains unsupervised because we do not assume prior knowledge of the noisy recording environment. In this context, our contribution is to propose a noise model based on alpha-stable distributions, instead of the more conventional Gaussian non-negative matrix factorization approach found in previous studies. We develop a Monte Carlo expectation-maximization algorithm for estimating the model parameters at test time. Experimental results show the superiority of the proposed approach both in terms of perceptual quality and intelligibility of the enhanced speech signal., 5 pages, 3 figures, audio examples and code available online : https://team.inria.fr/perception/research/icassp2019-asvae/. arXiv admin note: text overlap with arXiv:1811.06713
Published: 2019
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7. Audio source separation into the wild

Author: Laurent Girin, Sharon Gannot, Xiaofei Li, 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]), and Bar-Ilan University [Israël]
Subjects: Reverberation, Computer science, linear Gaussian models, audio source separation, nonnegative matrix factorization, 020206 networking & telecommunications, 02 engineering and technology, 01 natural sciences, Field (computer science), Non-negative matrix factorization, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0103 physical sciences, Synchronization (computer science), 0202 electrical engineering, electronic engineering, information engineering, Source separation, Electronic engineering, Robot, 010301 acoustics
Abstract: International audience; This review chapter is dedicated to multichannel audio source separation in real-life environment. We explore some of the major achievements in the field and discuss some of the remaining challenges. We will explore several important practical scenarios, e.g. moving sources and/or microphones, varying number of sources and sensors, high reverberation levels, spatially diffuse sources, and synchronization problems. Several applications such as smart assistants, cellular phones, hearing aids and robots, will be discussed. Our perspectives on the future of the field will be given as concluding remarks of this chapter.
Published: 2019
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8. List of Contributors

Author: Xavier Alameda-Pineda, Stefano Alletto, Vasileios Argyriou, Claudio Baecchi, Lorenzo Baraldi, Marco Bertini, Marc Bolaños, Pierre Bour, Luca Brayda, Alessio Brutti, Laura Cabrera Quiros, Victor Campos, Alejandro Cartas, Andrea Cavallaro, Shih-Fu Chang, Wen-Sheng Chu, Jeffrey F. Cohn, Marcella Cornia, Emile Cribelier, Rita Cucchiara, Alberto Del Bimbo, Antoine Deleforge, Eyal Dim, Mariella Dimiccoli, Itir Onal Ertugrul, Anna Esposito, Andrea Ferracani, Sharon Gannot, Maite Garolera, Ekin Gedik, Olga Gelonch, Jeffrey M. Girard, Laurent Girin, Xavier Giro-i-Nieto, Luca Giuliani, Furkan Gürpınar, Zakia Hammal, Hayley Hung, László A. Jeni, Kristiina Jokinen, Brendan Jou, Heysem Kaya, Walter Kellermann, Tsvi Kuflik, Xiaofei Li, Nicoletta Noceti, Francesca Odone, Gabriel Oliveira-Barra, Marcello Pelillo, Francesco Perrone, Emily Mower Provost, Petia Radeva, Elisa Ricci, Giorgio Roffo, Albert Ali Salah, Alexander Schmidt, Björn Schuller, Filomena Scibelli, Nicu Sebe, Lorenzo Seidenari, Giuseppe Serra, Joan Sosa-Garciá, Estefania Talavera, Mohammad Tayarani, Qi Tian, Jordi Torres, Andrea Trucco, Panagiotis Tzirakis, Tiberio Uricchio, Sebastiano Vascon, Alessandro Vinciarelli, Dong-Bach Vo, Graham Wilcock, Lingxi Xie, Stefanos Zafeiriou, and Biqiao Zhang
Published: 2019
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9. Online Localization of Multiple Moving Speakers in Reverberant Environments

Author: Bastien Mourgue, Laurent Girin, Sharon Gannot, 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]), Bar-Ilan University [Israël], and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: Reverberation, Computer science, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, Acoustic source localization, Mixture model, Speech processing, Motion capture, Complex normal distribution, 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], Rate of convergence, Computer Science::Sound, Feature (computer vision), reverberant environments, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 0202 electrical engineering, electronic engineering, information engineering, multiple moving speakers, sound-source localization, 0305 other medical science
Abstract: International audience; This paper addresses the problem of online multiple moving speakers localization in reverberant environments. The direct-path relative transfer function (DP-RTF), as defined by the ratio between the first taps of the convolutive transfer function (CTF) of two microphones, encodes the inter-channel direct-path information and is thus used as a localization feature being robust against reverberation. The CTF estimation is based on the cross-relation method. In this work, the recursive least-square method is proposed to solve the cross-relation problem, due to its relatively low computational cost and its good convergence rate. The DP-RTF feature estimated at each time-frequency bin is assumed to correspond to a single speaker. A complex Gaussian mixture model is used to assign each observed feature to one among several speakers. The recursive expectation-maximization algorithm is adopted to update online the model parameters. The method is evaluated with a new dataset containing multiple moving speakers, where the ground-truth speaker trajectories are recorded with a motion capture system.
Published: 2018
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10. Key Considerations In Designing A Speech Brain-Computer Interface

Author: Laurent Girin, Blaise Yvert, Stephan Chabardes, Thomas Hueber, Florent Bocquelet, 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]), 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 Centre Hospitalier Universitaire [Grenoble] (CHU)
Subjects: 0301 basic medicine, Speech production, Computer science, Speech recognition, Speech synthesis, computer.software_genre, 03 medical and health sciences, Neural prosthesis, 0302 clinical medicine, Physiology (medical), Aphasia, medicine, Humans, Speech, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Neural decoding, Silent speech, Brain–computer interface, General Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, Rehabilitation, Speech processing, 030104 developmental biology, Covert, Brain-Computer Interfaces, Neurocomputational speech processing, medicine.symptom, computer, 030217 neurology & neurosurgery
Abstract: International audience; Restoring communication in case of aphasia is a key challenge for neurotechnologies. To this end, brain-computer strategies can be envisioned to allow artificial speech synthesis from the continuous decoding of neural signals underlying speech imagination. Such speech brain-computer interfaces do not exist yet and their design should consider three key choices that need to be made: the choice of appropriate brain regions to record neural activity from, the choice of an appropriate recording technique, and the choice of a neural decoding scheme in association with an appropriate speech synthesis method. These key considerations are discussed here in light of (1) the current understanding of the functional neuroanatomy of cortical areas underlying overt and covert speech production, (2) the available literature making use of a variety of brain recording techniques to better characterize and address the challenge of decoding cor-tical speech signals, and (3) the different speech synthesis approaches that can be considered depending on the level of speech representation (phonetic, acoustic or articulatory) envisioned to be decoded at the core of a speech BCI paradigm.
Published: 2018
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11. Variational Bayesian Inference for Audio-Visual Tracking of Multiple Speakers

Author: Laurent Girin, Xavier Alameda-Pineda, Radu Horaud, Yutong Ban, 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), Vers des robots à l’intelligence sociale au travers de l’apprentissage, de la perception et de la commande (ROBOTLEARN), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-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), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), and European Project: 340113,EC:FP7:ERC,ERC-2013-ADG,VHIA(2014)
Subjects: FOS: Computer and information sciences, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Inference, Machine Learning (stat.ML), 02 engineering and technology, Latent variable, Bayesian inference, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, Joint probability distribution, Statistics - Machine Learning, Expectation–maximization algorithm, 0202 electrical engineering, electronic engineering, information engineering, Graphical model, Set (psychology), multiple object tracking, Dynamic Bayesian network, business.industry, audio-visual tracking, Applied Mathematics, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, dynamic Bayesian networks, Multimedia (cs.MM), Computational Theory and Mathematics, expectation-maximization, Video tracking, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], 020201 artificial intelligence & image processing, speaker diarization, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software, Computer Science - Multimedia, variational inference
Abstract: International audience; In this paper we address the problem of tracking multiple speakers via the fusion of visual and auditory information. We propose to exploit the complementary nature and roles of these two modalities in order to accurately estimate smooth trajectories of the tracked persons, to deal with the partial or total absence of one of the modalities over short periods of time, and to estimate the acoustic status - either speaking or silent - of each tracked person over time. We propose to cast the problem at hand into a generative audio-visual fusion (or association) model formulated as a latent-variable temporal graphical model. This may well be viewed as the problem of maximizing the posterior joint distribution of a set of continuous and discrete latent variables given the past and current observations, which is intractable. We propose a variational inference model which amounts to approximate the joint distribution with a factorized distribution. The solution takes the form of a closed-form expectation maximization procedure. We describe in detail the inference algorithm, we evaluate its performance and we compare it with several baseline methods. These experiments show that the proposed audio-visual tracker performs well in informal meetings involving a time-varying number of people.
Published: 2018
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12. Explaining the parameterized wiener filter with alpha-stable processes

Author: Laurent Girin, Antoine Liutkus, Roland Badeau, Mathieu Fontaine, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-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 ), 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]), Télécom ParisTech, Projet ANR KAMoulox, ANR-15-CE38-0003,KAMoulox,Démixage en ligne de larges archives sonores(2015), Badeau, Roland, Démixage en ligne de larges archives sonores - - KAMoulox2015 - ANR-15-CE38-0003 - AAPG2015 - VALID, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)
Subjects: Noise (signal processing), Noise reduction, Speech recognition, Wiener filter, Spectral density, Wiener deconvolution, 020206 networking & telecommunications, 02 engineering and technology, Weighting, Speech enhancement, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, Fourier transform, probability theory, denoising, 0202 electrical engineering, electronic engineering, information engineering, symbols, Wiener filtering, 0305 other medical science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, alpha-stable processes, Mathematics
Abstract: International audience; This paper introduces a new method for single-channel denoising that sheds new light on classical early developments on this topic that occurred in the 70’s and 80’s with Wiener filtering and spectral subtraction. Operating both in the short-time Fourier transform domain, these methods consist in estimating the power spectral density (PSD) of the noise without speech. Then, the clean speech signal is obtained by manipulating the corrupted time-frequency bins thanks to these noise PSD estimates. Theoretically grounded when using power spectra, these methods were subsequently generalized to magnitude spectra, or shown to yield better performance by weighting the PSDs in the so-called parameterized Wiener filter. Both these strategies were long considered ad-hoc. To the best of our knowledge, while we recently proposed an interpretation of magnitude processing, there is still no theoretical result that would justify the better performance of parameterized Wiener filters. Here, we show how the α-stable probabilistic model for waveforms naturally leads to these weighted filters and we provide a grounded and fast algorithm to enhance corrupted audio that compares favorably with classical denoising methods.
Published: 2017
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13. A High-Rate Data Hiding Technique for Uncompressed Audio Signals

Author: Laurent Girin, Jonathan Pinel, Cléo Baras, GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), 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 - Machines parlantes, Gestes oro-faciaux, Interaction Face-à-face, Communication augmentée (GIPSA-MAGIC), Département Parole et Cognition (GIPSA-DPC), ANR DreaM, and ANR-09-CORD-0006,DReaM,Le Disque Repensé pour l'Écoute Active de la Musique(2009)
Subjects: Audio signal, Computer science, Speech recognition, audio watermarking, data hiding, MDCT, General Engineering, Audio bit depth, Uncompressed video, Audio normalization, Information hiding, Sound quality, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, Music
Abstract: International audience
Published: 2014
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14. Fast and Accurate Direct MDCT to DFT Conversion With Arbitrary Window Functions

Author: Shuhua Zhang, Laurent Girin, 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 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), and ANR-09-CORD-0006,DReaM,Le Disque Repensé pour l'Écoute Active de la Musique(2009)
Subjects: Signal processing, Acoustics and Ultrasonics, Modified discrete cosine transform, Finite impulse response, MDCT, 020206 networking & telecommunications, 02 engineering and technology, Filter (signal processing), Toeplitz matrix, DFT, Window function, Discrete Fourier transform, FIR filtering, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Discrete cosine transform, 020201 artificial intelligence & image processing, Electrical and Electronic Engineering, Arithmetic, window function, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Mathematics
Abstract: International audience; In this paper, we propose a method for direct con- version of MDCT coefficients to DFT coefficients, without passing through time signal reconstruction. In contrast to previous work, this method is valid for any pair of MDCT and DFT window functions. It is based on the decomposition of the MDCT-to- DFT conversion matrices into a Toeplitz part plus a Hankel part. The latter is split, then mirrored and combined with the former to construct a global Toeplitz matrix. This leads to a fast FIR filtering implementation of the conversion process. The filter taps are DFT coefficients of window functions products, and concentrate most of their energy in a few low-frequency taps. The conversion can thus be efficiently approximated by keeping only a few most significant taps, as confirmed by numerical experiments: For example, for frame size of 2048, Hanning-windowed DFT is obtained from KBD-windowed MDCT with SNR over 60 dB when keeping only 20 taps.
Published: 2013
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15. Adaptation of a Gaussian Mixture Regressor to a New Input Distribution: Extending the C-GMR Framework

Author: Laurent Girin, 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]), and University of Trento [Trento]
Subjects: 0209 industrial biotechnology, Computer science, Gaussian, 02 engineering and technology, Gaussian mixture regression, Machine learning, computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, symbols.namesake, 020901 industrial engineering & automation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Expectation–maximization algorithm, [INFO]Computer Science [cs], GMM, Adaptation, EM algorithm, Adaptation (computer science), Gaussian Mixture Regression, business.industry, Inversion (meteorology), Distribution (mathematics), symbols, Artificial intelligence, 0305 other medical science, business, Algorithm, computer
Abstract: International audience; This paper addresses the problem of the adaptation of a Gaussian Mixture Regression (GMR) to a new input distribution, using a limited amount of input-only examples. We propose a new model for GMR adaptation, called Joint GMR (J-GMR), that extends the previously published framework of Cascaded GMR (C-GMR). We provide an exact EM training algorithm for the J-GMR. We discuss the merits of the J-GMR with respect to the C-GMR and illustrate its performance with experiments on speech acoustic-to-articulatory inversion.
Published: 2017
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16. Deep neural networks for automatic detection of screams and shouted speech in subway trains

Author: Pierre Laffitte, David Sodoyer, Charles Tatkeu, Laurent Girin, 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, 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: RECONNAISSANCE DE SON, Computer science, Speech recognition, 02 engineering and technology, Task (project management), 030507 speech-language pathology & audiology, 03 medical and health sciences, Deep belief network, DETECTION DE CRIS, 0202 electrical engineering, electronic engineering, information engineering, DETECTION D'INCIDENT, METRO, ENVIRONNEMENT TRANSPORT, Voice activity detection, 020206 networking & telecommunications, RESEAU DE NEURONES, DETECTION D'EVENEMENTS SONORES AUDIO, ENVIRONNEMENT SONORE, TRANSPORT FERROVIAIRE, BRUIT, Deep neural networks, DETECTION D'EVENEMENT SONORE, Train, Noise (video), 0305 other medical science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, DEEP BELIEF NETWORKS
Abstract: IEEE ICASSP 2016 - International Conference on Acoustics, Speech and Signal Processing, Shanghai, Chine, 20-/03/2016 - 25/03/2016; International audience; Deep Neural Networks (DNNs) have recently become a popular technique for regression and classification problems. Their capacity to learn high-order correlations between input and output data proves to be very powerful for automatic speech recognition. In this paper we investigate the use of DNNs for automatic scream and shouted speech detection, within the framework of surveillance systems in public transportation. We recorded a database of sounds occurring in subway trains in real conditions of exploitation and used DNNs to classify the sounds into screams, shouts and other categories. We report encouraging results, given the difficulty of the task, especially when a high level of surrounding noise is present.; Les réseaux de neurones profonds sont devenues récemment une technique populaire pour les problèmes de régression et de classification. Leur capacité d'apprendre des corrélations d'ordre éleÎ entre des entrées et des données de sortie s'aÏre être très un puissant outil pour reconnaissance automatique de la parole. Dans cet article, nous étudions l'utilisation des réseaux de neurones profonds pour la détection automatique de cris et de parole criée dans le cadre de systèmes de surveillance dans les transports publics. Pour cela, une base de données sonores a été enregistrée dans une rame de métro en condition réelle d'exploitation. Dans ce contexte, la détection de cri est réalisée via un classement de divers types de production de la parole dont des cris. Nous obtenons des résultats encourageants étant donné la difficulté de la tâche, en particulier vis-à-vis du haut niveau de bruit sonore environnant.
Published: 2016
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17. Visual voice activity detection as a help for speech source separation from convolutive mixtures

Author: Christian Jutten, Bertrand Rivet, Laurent Girin, GIPSA - Machines Parlantes, Agents Communicants & Interaction Face-à-face (GIPSA-MPACIF), 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 - Signal Images Physique (GIPSA-SIGMAPHY), and Département Images et Signal (GIPSA-DIS)
Subjects: Linguistics and Language, Computer science, Speech recognition, Speech enhancement, 02 engineering and technology, 01 natural sciences, Signal, Language and Linguistics, Visual speech processing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Interference (communication), 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, Source separation, 010301 acoustics, Convolutive mixtures, Signal processing, Voice activity detection, Communication, Detector, Highly non-stationary environment, 020206 networking & telecommunications, Speech processing, Computer Science Applications, Speech source separation, Modeling and Simulation, Physical Sciences, Computer Vision and Pattern Recognition, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software
Abstract: International audience; Audio–visual speech source separation consists in mixing visual speech processing techniques (e.g., lip parameters tracking) with source separation methods to improve the extraction of a speech source of interest from a mixture of acoustic signals. In this paper, we present a new approach that combines visual information with separation methods based on the sparseness of speech: visual information is used as a voice activity detector (VAD) which is combined with a new geometric method of separation. The proposed audio–visual method is shown to be efficient to extract a real spontaneous speech utterance in the difficult case of convolutive mixtures even if the competing sources are highly non-stationary. Typical gains of 18–20 dB in signal to interference ratios are obtained for a wide range of (2 × 2) and (3 × 3) mixtures. Moreover, the overall process is computationally quite simpler than previously proposed audio–visual separation schemes.
Published: 2007
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18. A variational EM algorithm for the separation of moving sound sources

Author: Laurent Girin, Dionyssos Kounades-Bastian, Sharon Gannot, 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)-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), 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), 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: Mathematical optimization, Computer science, 02 engineering and technology, variational EM, Matrix decomposition, 030507 speech-language pathology & audiology, 03 medical and health sciences, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], moving sources, Expectation–maximization algorithm, 0202 electrical engineering, electronic engineering, information engineering, Source separation, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Probabilistic logic, Estimator, 020206 networking & telecommunications, Kalman filter, Audio-source separation, Complex normal distribution, Kalman smoother, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Algorithm design, 0305 other medical science, time-varying mixing filters, Algorithm, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract: International audience; This paper addresses the problem of separation of moving sound sources. We propose a probabilistic framework based on the complex Gaussian model combined with non-negative matrix factorization. The properties associated with moving sources are modeled using time-varying mixing filters described by a stochastic temporal process. We present a variational expectation-maximization (VEM) algorithm that employs a Kalman smoother to estimate the mixing filters. The sound sources are separated by means of Wiener filters, built from the estimators provided by the proposed VEM algorithm. Preliminary experiments with simulated data show that, while for static sources we obtain results comparable with the base-line method of Ozerov et al., in the case of moving source our method outperforms a piece-wise version of the baseline method.
Published: 2015
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19. Developing an audio-visual speech source separation algorithm

Author: David Sodoyer, Laurent Girin, Christian Jutten, and Jean-Luc Schwartz
Subjects: Linguistics and Language, Computer science, Communication, Speech recognition, Coherence (statistics), Speech processing, Blind signal separation, Language and Linguistics, Computer Science Applications, Speech enhancement, Identification (information), Modeling and Simulation, Face (geometry), Source separation, Computer Vision and Pattern Recognition, Focus (optics), Algorithm, Software
Abstract: Looking at the speaker’s face is useful to hear better a speech signal and extract it from competing sources before identification. This might result in elaborating new speech enhancement or extraction techniques exploiting the audio-visual coherence of speech stimuli. In this paper, a novel algorithm plugging audio-visual coherence estimated by statistical tools on classical blind source separation algorithms is presented, and its assessment is described. We show, in the case of additive mixtures, that this algorithm performs better than classical blind tools both when there are as many sensors as sources, and when there are less sensors than sources. Audio-visual coherence enables a focus on the speech source to extract. It may also be used at the output of a classical source separation algorithm, to select the “best” sensor with reference to a target source.
Published: 2004
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20. Joint Matrix Quantization of Face Parameters and LPC Coefficients for Low Bit Rate Audiovisual Speech Coding

Author: Laurent Girin
Subjects: Acoustics and Ultrasonics, Computer science, Speech recognition, Quantization (signal processing), Speech coding, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Signal compression, Linear predictive coding, Speech processing, Videotelephony, Computer Science::Sound, Computer Science::Multimedia, Videophone, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Software, Data compression
Abstract: A key problem for videophony, that is telephony including the processing of images of the speaker's face in addition to acoustic speech, concerns signal compression for transmission. In such systems, audio and video compression are separately achieved by using both audio and video coders. In this paper, an audio-visual approach to this problem is considered, since we claim that the fundamental property of coherence (redundancy) between the two modalities of speech should be exploited by coding systems. We consider the framework of parametric analysis, modeling and synthesis of talking faces, which allows efficient representation of video information. Thus, we propose to jointly encode several face parameters, namely lip shape geometric descriptors, together with sets of audio coefficients, namely quite usual LPC parameters. The definition of an audiovisual distance between vectors of concatenated audio and video parameters allows to generate audiovisual single stage vector and matrix quantizers by using the generalized Lloyd algorithm. Calculation of video and audio mean distortion measures shows a significant gain in quantization accuracy and/or resolution compared to separate video and audio quantization. An alternative sub-optimal tree-like structure for audiovisual joint coding is also tested and yields interesting results while decreasing the computational complexity of the quantization process.
Published: 2004
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21. Audio-visual enhancement of speech in noise

Author: Laurent Girin, Gang Feng, and Jean-Luc Schwartz
Subjects: Speech Acoustics, Audio mining, Acoustics and Ultrasonics, Artificial neural network, Computer science, Gaussian, Speech recognition, Speech coding, Filter (signal processing), Intelligibility (communication), Communications system, Speech enhancement, symbols.namesake, Additive white Gaussian noise, Arts and Humanities (miscellaneous), symbols
Abstract: A key problem for telecommunication or human–machine communication systems concerns speech enhancement in noise. In this domain, a certain number of techniques exist, all of them based on an acoustic-only approach—that is, the processing of the audio corrupted signal using audio information (from the corrupted signal only or additive audio information). In this paper, an audio-visual approach to the problem is considered, since it has been demonstrated in several studies that viewing the speaker’s face improves message intelligibility, especially in noisy environments. A speech enhancement prototype system that takes advantage of visual inputs is developed. A filtering process approach is proposed that uses enhancement filters estimated with the help of lip shape information. The estimation process is based on linear regression or simple neural networks using a training corpus. A set of experiments assessed by Gaussian classification and perceptual tests demonstrates that it is indeed possible to enhance simple stimuli (vowel-plosive-vowel sequences) embedded in white Gaussian noise.
Published: 2001
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22. Restaurer la parole avec une interface cerveau–machine : étapes et challenges

Author: Laurent Girin, Florent Bocquelet, Stephan Chabardes, Blaise Yvert, and Thomas Hueber
Subjects: 03 medical and health sciences, 0302 clinical medicine, Neurology, Physiology (medical), 05 social sciences, 0501 psychology and cognitive sciences, Neurology (clinical), General Medicine, 030217 neurology & neurosurgery, 050105 experimental psychology
Abstract: En France, 300 000 personnes souffrent d’un trouble de la parole et plus de 5 millions de par le monde. Ces troubles surviennent le plus souvent suite a un accident vasculaire cerebral ayant endommage le reseau cortical du langage. Cependant, des aphasies severes sont egalement observees chez des personnes ou ce reseau n’a pas ete endommage, notamment dans le cas d’un syndrome d’enfermement (« locked-in »), de maladies neurodegeneratives telles que la sclerose laterale amyotrophique ou la maladie de Parkinson, de myopathies, ou encore de comas. Dans ce cas, il est possible d’envisager de restaurer la parole par un paradigme d’interface cerveau–machine (BCI) ou les activites corticales sont decodees pour predire ce que souhaite dire la personne [3] . Dans cette perspective, nous developpons une strategie visant a enregistrer les aires motrices de la parole controlant les articulateurs du conduit vocal, et a decoder leurs activites pour controler un synthetiseur vocal articulatoire. Les differentes etapes de ce projet seront discutees, ainsi que les premiers resultats deja obtenus en termes de synthese vocale artificielle en temps reel [1] et de decodage des activites corticales des aires de la parole. Nous soulignerons egalement certaines questions ethiques associees au projet et plus largement au developpement de neuroprotheses et d’interfaces cerveau-machine en general [2] .
Published: 2016
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23. A mediating role of the auditory dorsal pathway in selective adaptation to speech: a state-dependent transcranial magnetic stimulation study

Author: Laurent Girin, Vincent L. Gracco, Marc Sato, Krystyna Grabski, Pascale Tremblay, GIPSA - Perception, Contrôle, Multimodalité et Dynamiques de la parole (GIPSA-PCMD), 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), Département de Réhabilition, Université Laval [Québec] (ULaval), School of Communication Sciences and Disorders, McGill University = Université McGill [Montréal, Canada], and GIPSA - Machines parlantes, Gestes oro-faciaux, Interaction Face-à-face, Communication augmentée (GIPSA-MAGIC)
Subjects: Adult, Male, Speech perception, Auditory Pathways, medicine.medical_treatment, [SHS.PSY]Humanities and Social Sciences/Psychology, Stimulus (physiology), Auditory cortex, 050105 experimental psychology, Premotor cortex, 03 medical and health sciences, Superior temporal gyrus, Young Adult, 0302 clinical medicine, Supramarginal gyrus, medicine, Reaction Time, Humans, 0501 psychology and cognitive sciences, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, Molecular Biology, Auditory Cortex, [SCCO.NEUR]Cognitive science/Neuroscience, General Neuroscience, 05 social sciences, [SCCO.LING]Cognitive science/Linguistics, Adaptation, Physiological, Transcranial Magnetic Stimulation, Transcranial magnetic stimulation, medicine.anatomical_structure, Acoustic Stimulation, [SCCO.PSYC]Cognitive science/Psychology, Speech Perception, Female, Neurology (clinical), Neurocomputational speech processing, Psychology, Neuroscience, 030217 neurology & neurosurgery, Developmental Biology, Cognitive psychology
Abstract: International audience; In addition to sensory processing, recent neurobiological models of speech perception postulate the existence of a left auditory dorsal processing stream, linking auditory speech representations in the auditory cortex with articulatory representations in the motor system, through sensorimotor interaction interfaced in the supramarginal gyrus and/or the posterior part of the superior temporal gyrus. The present state-dependent transcranial magnetic stimulation study is aimed at determining whether speech recognition is indeed mediated by the auditory dorsal pathway, by examining the causal contribution of the left ventral premotor cortex, supramarginal gyrus and posterior part of the superior temporal gyrus during an auditory syllable identification/categorization task. To this aim, participants listened to a sequence of /ba/ syllables before undergoing a two forced-choice auditory syllable decision task on ambiguous syllables (ranging in the categorical boundary between /ba/ and /da/). Consistent with previous studies on selective adaptation to speech, following adaptation to /ba/, participants responses were biased towards /da/. In contrast, in a control condition without prior auditory adaptation no such bias was observed. Crucially, compared to the results observed without stimulation, single-pulse transcranial magnetic stimulation delivered at the onset of each target stimulus interacted with the initial state of each of the stimulated brain area by enhancing the adaptation effect. These results demonstrate that the auditory dorsal pathway contribute to auditory speech adaptation.
Published: 2012
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24. A simple hybrid acoustic/morphologically-constrained technique for the synthesis of stop consonants in various vocalic contexts

Author: Frédéric Berthommier, Laurent Girin, and Louis-Jean Boë
Published: 2012
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25. Principles of Speech Coding

Author: Laurent Girin and Gang Feng
Subjects: Engineering, Transmission (telecommunications), business.industry, Speech coding, Electrical engineering, Electronic engineering, business
Published: 2010
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26. Long-term flexible 2D cepstral modeling of speech spectral amplitudes

Author: Mohammad Firouzmand, Laurent Girin, Girin, Laurent, GIPSA - Machines Parlantes, Agents Communicants & Interaction Face-à-face (GIPSA-MPACIF), 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), 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: Masking (art), [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, speech coding, Speech recognition, Speech coding, Speech synthesis, 02 engineering and technology, computer.software_genre, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, speech synthesis, Cepstrum, 0202 electrical engineering, electronic engineering, information engineering, Discrete cosine transform, Envelope (mathematics), speech processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, speech analysis, 020206 networking & telecommunications, Speech processing, speech modeling, Amplitude, Spectral envelope, 0305 other medical science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computer, Algorithm
Abstract: International audience; This paper presents a method for modeling the envelope of spectral amplitude parameters of speech signals in "two dimensions" (2D). It consists of two cascaded modelings: the first one along the frequency axis is the usual cepstrum technique, which consists of modeling the log-scaled spectral envelope with a Discrete Cosine Model (DCM). The second one, along the time axis, consists of modeling the trajectory of the envelope DCM coefficients by another similar DCM model. An iterative algorithm is proposed to optimally fit this 2D-model to the data according to a perceptual criterion based on frequency masking. This approach is shown to provide an efficient and flexible representation of spectral amplitude parameters in terms of coefficient rates, while providing good signal quality, opening new perspectives in very-low bit-rate sinusoidal speech coding.
Published: 2008
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27. Perceptually Weighted Long Term Modeling of Sinusoidal Speech Amplitude Trajectories

Author: Mohammad Firouzmand and Laurent Girin
Subjects: Noise, Masking threshold, Computer Science::Sound, Distortion, Speech recognition, Speech coding, Context (language use), Speech synthesis, Sinusoidal model, computer.software_genre, computer, Mathematics, Interpolation
Abstract: In this paper, the problem of modeling the trajectory of the amplitudes of speech signals is addressed within the context of the sinusoidal model of speech. A long-term model of the trajectory of the amplitude of the partials is proposed for each entire voiced section of speech, contrary to standard models, which are defined on a frame-by-frame basis. The complete analysis-modeling-synthesis process is presented. We compare a DCT-based long-term model with classical (frame-by-frame) interpolation schemes, given that the analysis process is identical in both cases. Perceptual constraints are taken into account since the distortion criterion in this approach is the level of modeling noise above the masking threshold. Promising results are given and the interest of the presented models for speech coding and watermarking applications is discussed.
Published: 2006
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28. Solving The Inderterminations Of Blind Source Separation Of Convolutive Speech Mixtures

Author: Christian Jutten, Laurent Girin, and Bertrand Rivet
Subjects: Signal processing, Computer science, Estimation theory, Speech recognition, computer.software_genre, Speech processing, Blind signal separation, Speech enhancement, Computer Science::Sound, Frequency separation, Frequency domain, Source separation, Audio signal processing, computer
Abstract: Looking at the speaker's face seems useful for hearing a speech signal better and extracting it from competing sources before identification. We present a novel algorithm plugging the audiovisual coherence of speech signals, estimated by statistical tools, on audio blind source separation (BSS) algorithms in the difficult case of convolutive mixtures. The algorithm mainly works in the frequency (transform) domain, where the convolutive mixture becomes an additive mixture for each frequency channel. Frequency by frequency separation is made by an audio BSS algorithm, and the audiovisual information is used to solve the standard source permutation and scale factor problems at the output of the separation stage, for each frequency. The proposed method is shown to be efficient in the case of 2/spl times/2 convolutive mixtures.
Published: 2006
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29. Using audiovisual speech processing to improve the robustness of the separation of convolutive speech mixtures

Author: Laurent Girin, Jean-Luc Schwartz, Christian Jutten, and Bertrand Rivet
Subjects: Voice activity detection, Computer Science::Sound, Frequency separation, Computer science, Robustness (computer science), Speech recognition, Speech coding, Audiovisual speech, Audio signal processing, computer.software_genre, Speech processing, computer, Blind signal separation
Abstract: Looking at the speaker's face seems useful in hearing better a speech signal and extract it from the competing sources before identification. In this paper, we present a novel algorithm plugging audiovisual coherence of speech signals, estimated by statistical tools, on audio blind source separation (BSS) algorithms in the difficult case of convolutive mixtures. The algorithm mainly works in the frequency (transform) domain, where the convolutive mixture becomes an additive mixture for each frequency channel. Frequency by frequency separation is made by an audio BSS algorithm, and the audiovisual information is used to solve the standard source permutation problem at the output of the separation stage, for each frequency. The proposed method is shown to be efficient in the case of 2 /spl times/ 2 convolutive mixtures.
Published: 2005
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30. Speech extraction based on ICA and audio-visual coherence

Author: C. Jutten, Jean-Luc Schwartz, Laurent Girin, and David Sodoyer
Subjects: Voice activity detection, Computer Science::Sound, Computer science, Speech recognition, Speech coding, Acoustic model, Coherence (statistics), Linear predictive coding, Audio signal processing, computer.software_genre, Speech processing, computer, Blind signal separation
Abstract: We present a new approach to the source separation problem for multiple speech signals. Using the extra visual information of the speaker's face, the method aims to extract an acoustic speech signal from other acoustic signals by exploiting its coherence with the speaker's lip movements. We define a statistical model of the joint probability of visual and spectral audio input for quantifying the audio-visual coherence. Then, separation can be achieved by maximising this joint probability. Experiments on additive mixtures of 2, 3 and 5 sources show that the algorithm performs well, and systematically better than the classical BSS algorithm JADE.
Published: 2003
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31. Audiovisual speech enhancement: new advances using multi-layer perceptrons

Author: Laurent Girin, Jean-Luc Schwartz, G. Feng, and Laurent Varin
Subjects: Computer science, business.industry, Speech recognition, Pattern recognition, Linear prediction, White noise, Intelligibility (communication), Perceptron, computer.software_genre, Sensor fusion, Distance measures, Speech enhancement, Artificial intelligence, Audio signal processing, business, computer
Abstract: This paper deals with the improvement of a noisy speech enhancement system based on the fusion of auditory and visual information. The system was presented in previous papers and implemented with a simple stimuli corrupted with white noise. Its principle consists of an analysis-enhancement-synthesis process based on a linear prediction (LP) model of the signal: the LP filter is enhanced thanks to associative tools that estimate the LP cleaned parameters from both noisy audio and lip shape information. The structure of the system is reviewed and we focus on the improvement that concerns the associators: multi-layers perceptrons are used instead of linear regression. It is shown that in the context of VCV transitions corrupted with white noise, the performances of the system are improved in terms of the intelligibility gain, distance measures and classification tests.
Published: 2002
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32. Fusion of auditory and visual information for noisy speech enhancement: a preliminary study of vowel transitions

Author: Laurent Girin, G. Feng, and Jean-Luc Schwartz
Subjects: Speech enhancement, Estimation theory, Computer science, business.industry, Vowel, Speech recognition, Noise reduction, Context (language use), Pattern recognition, Artificial intelligence, White noise, Speech processing, business
Abstract: This paper deals with a noisy speech enhancement technique based on the fusion of auditory and visual information. We first present the global structure of the system, and then we focus on the tool we used to melt both sources of information. The whole noise reduction system is implemented in the context of vowel transitions corrupted with white noise. A complete evaluation of the system in this context is presented, including distance measures, Gaussian classification scores, and a perceptive test. The results are very promising.
Published: 2002
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33. An audio-visual distance for audio-visual speech vector quantization

Author: Laurent Girin, Elodie Foucher, and G. Feng
Subjects: Voice activity detection, Exploit, business.industry, Computer science, Existential quantification, Speech recognition, Speech coding, Vector quantization, Speech processing, Computer Science::Sound, Computer Science::Multimedia, Computer vision, Artificial intelligence, business, Decoding methods, Coding (social sciences)
Abstract: Speech is both an acoustic and a visual signal, and there exists some complementarity and redundancy between the two modalities. In the speech coding domain, it is of great interest to use this redundancy to improve speech coder performance. In this paper, we consider some audio and video joint coding process based on an audio-visual vector quantization. The method is shown to exploit quite well the audio-visual redundancy as it can reduce the bit rate while decreasing the quantization error. A notion of audio-visual distance has to be introduced and adapted to the different nature of the data. It is defined from an existing audio distance and a new visual distance, which is particularly focussed.
Published: 2002
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34. Speech signals separation: a new approach exploiting the coherence of audio and visual speech

Author: Laurent Girin, Jean-Luc Schwartz, and A. Allard
Subjects: Voice activity detection, Computer science, Speech recognition, Speech coding, Source separation, Acoustic model, Coherence (statistics), Speaker recognition, Speech processing, Independent component analysis
Abstract: We present a new approach to the source separation problem in the case of multiple speech signals. The method is based on the use of automatic lip reading: the objective is to extract an acoustic speech signal from other acoustic signals by exploiting its coherence with the speaker's lip movements. For this aim, a statistical model is used to quantify this coherence. The results, while very preliminary, are encouraging. They show that this method can achieve a good separation of a speech source in the case of simple 2/spl times/2 additive mixtures. Moreover, it presents some interesting complementarity with traditional pure audio techniques.
Published: 2002
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35. A signal processing system for having the sound 'pop-out' in noise thanks to the image of the speaker's lips: new advances using multi-layer perceptrons

Author: Laurent Girin, Laurent Varin, Gang Feng, and Jean-Luc Schwartz
Published: 1998
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36. Noisy speech enhancement by fusion of auditory and visual information: a study of vowel transitions

Author: Laurent Girin, Gang Feng, and Jean-Luc Schwartz
Published: 1997
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37. An analysis of visual speech information applied to voice activity detection

Author: Laurent Girin, Christian Jutten, David Sodoyer, Jean-Luc Schwartz, Bertrand Rivet, Institut de la communication parlée (ICP), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3, Laboratoire des images et des signaux (LIS), Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), and Girin, Laurent
Subjects: Signal processing, Voice activity detection, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, Speech processing, Facial recognition system, Voice analysis, Speech enhancement, Background noise, Silence, Noise, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Gesture recognition, otorhinolaryngologic diseases, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract: We present a new approach to the voice activity detection (VAD) problem for speech signals embedded in non-stationary noise. The method is based on automatic lipreading: the objective is to detect voice activity or non-activity by exploiting the coherence between the speech acoustic signal and the speaker's lip movements. From a comprehensive analysis of lip shape parameters during speech and non-speech events, we show that a single appropriate visual parameter, defined to characterize the lip movements, can be used for the detection of sections of voice activity or more precisely, for the detection of silence sections. Detection scores obtained on spontaneous speech confirm the efficiency of the visual voice activity detector (VVAD).

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