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Your search keyword '"Perotin, Lauréline"' showing total 3 results
Start Over Author "Perotin, Lauréline" Topic 0202 electrical engineering, electronic engineering, information engineering
3 results on '"Perotin, Lauréline"'

1. Regression versus classification for neural network based audio source localization

Author

Alexandre Défossez, Alexandre Guerin, Emmanuel Vincent, Romain Serizel, Laureline Perotin, Perotin, Lauréline, 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), Laboratoire d'informatique de l'école normale supérieure (LIENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Orange Labs [Cesson-Sévigné], Orange Labs, IEEE, Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), 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), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)

Subjects
Mean squared error, soft target, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], law.invention, 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, law, training criterion, 0202 electrical engineering, electronic engineering, information engineering, Cartesian coordinate system, angular loss, Cost-sensitive classification, Artificial neural network, Angular distance, Probabilistic logic, Spherical coordinate system, 020206 networking & telecommunications, Grid, Regression, Direction-of-arrival, 0305 other medical science, Algorithm
Abstract

International audience; We compare the performance of regression and classification neural networks for single-source direction-of-arrival estimation. Since the output space is continuous and structured, regression seems more appropriate. However, classification on a discrete spherical grid is widely believed to perform better and is predominantly used in the literature. For regression, we propose two ways to account for the spherical geometry of the output space based either on the angular distance between spherical coordinates or on the mean squared error between Cartesian coordinates. For classification, we propose two alternatives to the classical one-hot encoding framework: we derive a Gibbs distribution from the squared angular distance between grid points and use the corresponding probabilities either as soft targets or as cross-entropy weights that retain a clear probabilis-tic interpretation. We show that regression on Cartesian coordinates is generally more accurate, except when localized interference is present, in which case classification appears to be more robust.

Published
2019

2. CRNN-based joint azimuth and elevation localization with the Ambisonics intensity vector

Author

Emmanuel Vincent, Romain Serizel, Laureline Perotin, Alexandre Guerin, Orange Labs [Cesson-Sévigné], Orange Labs, 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), 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), Perotin, Lauréline, 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), and 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)

Subjects
Reverberation, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Ambisonics, Computer science, Microphone, acoustic intensity, Direction of arrival, 020206 networking & telecommunications, CRNN, 02 engineering and technology, Sound intensity, direction of arrival, Azimuth, first-order Ambisonics (FOA), 030507 speech-language pathology & audiology, 03 medical and health sciences, Noise, Recurrent neural network, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 0305 other medical science, Algorithm
Abstract

International audience; We present a source localization system for first-order Ambisonics (FOA) contents based on a stacked convolutional and recurrent neural network (CRNN). We propose to use as input to the CRNN the FOA acoustic intensity vector, which is easy to compute and closely linked to the sound direction of arrival (DoA). The system estimates the DoA of a point source in both azimuth and elevation. We conduct an experimental evaluation in configurations including reverberation, noise, and various speaker w.r.t. microphone orientations. The results show that the proposed architecture and input allow the network to return accurate location estimates in realistic conditions compared to another recent CRNN-based system.

Published
2018

3. Multichannel Speech Separation with Recurrent Neural Networks from High-Order Ambisonics Recordings

Author

Emmanuel Vincent, Alexandre Guerin, Romain Serizel, Laureline Perotin, 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), Orange Labs [Cesson-Sévigné], Orange Labs, and Perotin, Lauréline

Subjects
Artificial neural network, Spatial filter, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, Ambisonics, Speech recognition, Word error rate, 020206 networking & telecommunications, 02 engineering and technology, Harmonic analysis, 030507 speech-language pathology & audiology, 03 medical and health sciences, Recurrent neural network, high-order ambisonics (HOA), [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, Source separation, multichannel filtering, LSTM, 0305 other medical science, Speech separation, Communication channel
Abstract

International audience; We present a source separation system for high-order ambisonics (HOA) contents. We derive a multichannel spatial filter from a mask estimated by a long short-term memory (LSTM) recurrent neural network. We combine one channel of the mixture with the outputs of basic HOA beamformers as inputs to the LSTM, assuming that we know the directions of arrival of the directional sources. In our experiments, the speech of interest can be corrupted either by diffuse noise or by an equally loud competing speaker. We show that adding as input the output of the beamformer steered toward the competing speech in addition to that of the beamformer steered toward the target speech brings significant improvements in terms of word error rate.

Published
2018
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