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6. Asteroid: the PyTorch-based audio source separation toolkit for researchers

Author

Ariel Frank, Emmanuel Vincent, Fabian-Robert Stöter, Mathieu Hu, Joris Cosentino, Manuel Pariente, Samuele Cornell, Sunit Sivasankaran, David Ditter, Efthymios Tzinis, Juan M. Martín-Doñas, Antoine Deleforge, Michel Olvera, Jens Heitkaemper, 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), Università Politecnica delle Marche [Ancona] (UNIVPM), Department of Electrical and Computer Engineering [Urbana] (University of Illinois), University of Illinois at Urbana-Champaign [Urbana], University of Illinois System-University of Illinois System, University of Paderborn, 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), Universidad de Granada = University of Granada (UGR), University of Hamburg, Technion - Israel Institute of Technology [Haifa], Grid'5000, 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), 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), 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), and University of Granada [Granada]

Subjects
FOS: Computer and information sciences, Sound (cs.SD), Computer science, open-source software, 020207 software engineering, 02 engineering and technology, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], end-to-end, Computer Science - Sound, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Speech enhancement, Computer engineering, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Audio and Speech Processing (eess.AS), Asteroid, source separation, [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Source separation, 020201 artificial intelligence & image processing, speech enhancement, Software architecture, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

This paper describes Asteroid, the PyTorch-based audio source separation toolkit for researchers. Inspired by the most successful neural source separation systems, it provides all neural building blocks required to build such a system. To improve reproducibility, Kaldi-style recipes on common audio source separation datasets are also provided. This paper describes the software architecture of Asteroid and its most important features. By showing experimental results obtained with Asteroid's recipes, we show that our implementations are at least on par with most results reported in reference papers. The toolkit is publicly available at https://github.com/mpariente/asteroid ., Submitted to Interspeech 2020

Published
2020

8. MEMS analogous micro-patterning of thermotropic nematic liquid crystalline elastomer films using a fluorinated photoresist and a hard mask process

Author

Christopher K. Ober, Rudolf Zentel, David Ditter, Andreas Best, Kaloian Koynov, Hans Zappe, and Wei-Liang Chen

Subjects
Microelectromechanical systems, Materials science, 02 engineering and technology, General Chemistry, Photoresist, 010402 general chemistry, 021001 nanoscience & nanotechnology, 01 natural sciences, Thermotropic crystal, 0104 chemical sciences, Micrometre, chemistry.chemical_compound, chemistry, Liquid crystal, Etching (microfabrication), Materials Chemistry, Composite material, 0210 nano-technology, Layer (electronics), Hydrogen silsesquioxane
Abstract

In this work, we present a method to pattern liquid crystal elastomers (LCEs) in the micrometer range without using any mechanical processing steps to prepare micron sized LCE actuators compatible with microelectromechanical system (MEMS) technology. Multi-layer spin-coating processes are developed to synthesise and structure 300–3500 nm thick LCE films. A water soluble sacrificial layer, a photoalignment layer and a LCE formulation, which is polymerised and crosslinked in its liquid crystal phase, are spin-coated successively onto a substrate. A fluorinated photoresist layer is used to structure LCE films with thicknesses up to 700 nm in a photolithographic and etching process. For thicker LCE films a hard mask process, using hydrogen silsesquioxane (HSQ) as hard mask, is used. Film thicknesses and homogeneities are analysed with profilometry. Actuation motions of LCE layers are investigated before and after patterning and LCE patterns are investigated via (polarised optical) microscopy (POM), scanning electron microscopy (SEM) and profilometry. A resolution of 1.5–2.0 microns is achieved with the described techniques, which make deformable micron sized LCE actuators of variable shape and director orientation accessible. The presented results demonstrate the potential of LCEs in MEMS devices.

Published
2017
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9. A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet

Author

Timo Gerkmann and David Ditter

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), Computer science, Multi phase, Speech recognition, Phase (waves), Contrast (statistics), 020206 networking & telecommunications, 02 engineering and technology, Filter bank, Computer Science - Sound, Machine Learning (cs.LG), 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, Time domain, Center frequency, 0305 other medical science, Encoder, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

In this work, we investigate if the learned encoder of the end-to-end convolutional time domain audio separation network (Conv-TasNet) is the key to its recent success, or if the encoder can just as well be replaced by a deterministic hand-crafted filterbank. Motivated by the resemblance of the trained encoder of Conv-TasNet to auditory filterbanks, we propose to employ a deterministic gammatone filterbank. In contrast to a common gammatone filterbank, our filters are restricted to 2 ms length to allow for low-latency processing. Inspired by the encoder learned by Conv-TasNet, in addition to the logarithmically spaced filters, the proposed filterbank holds multiple gammatone filters at the same center frequency with varying phase shifts. We show that replacing the learned encoder with our proposed multi-phase gammatone filterbank (MP-GTF) even leads to a scale-invariant source-to-noise ratio (SI-SNR) improvement of 0.7 dB. Furthermore, in contrast to using the learned encoder we show that the number of filters can be reduced from 512 to 128 without loss of performance., Comment: Accepted at ICASSP 2020

Published
2019
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10. Influences of Ortho‐Fluoroazobenzenes on Liquid Crystalline Phase Stability and 2D (Planar) Actuation Properties of Liquid Crystalline Elastomers

Author

David Ditter, Lukas B. Braun, and Rudolf Zentel

Subjects
540 Chemistry and allied sciences, Materials science, Polymers and Plastics, Liquid crystalline, Phase stability, Organic Chemistry, Condensed Matter Physics, Elastomer, Planar, Chemical engineering, 540 Chemie, Polymer chemistry, Materials Chemistry, Physical and Theoretical Chemistry
Published
2019
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