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Neural Information Processing Scaled for Bioacoustics : NIPS4B
Hervé GLOTIN · Yann LeCun · Thierry Artières · Stephane Mallat · Ofer Tchernichovski · Xanadu Halkias

Tue Dec 10 07:30 AM -- 06:30 PM (PST) @ Harrah's Tahoe C
Event URL: http://sabiod.org/nips4b »

Bioacoustic data science aims at modeling animal sounds for neuroethology and biodiversity assessment. It has received increasing attention due to its diverse potential benefits. It is steadily required by regulatory agencies for timely monitoring of environmental impacts from human activities. Given the complexity of the collected data along with the numerous species and environmental contexts, bioacoustics requires robust information processing.

The features and biological significance of animal sounds, are constrained by the physics of sound production and propagation, and evolved through the processes of natural selection. This yields to new paradigms such as curriculum song learning, predator-prey acoustic loop, etc. NIPS4B solidifies an innovative computational framework by focusing on the principles of information processing, if possible in an inheretly hierarchical manner or with physiological parallels: Deep Belief Networks (DBN), Sparse Auto Encoders (SAE), Convolutional Networks (ConNet), Scattering transforms etc. It encourages interdisciplinary, scientific exchanges and foster collaborations, bringing together experts from machine learning and computational auditory scene analysis, within animal sound and communication systems.

One challenge concerns bird classification (on Kaggle): identify 87 species of Provence (recordings Biotope SA). It is the biggest bird song challenge according to our knowledge, more complex than ICML4B (sabiod.org/ICML4B2013proceedings.pdf). A second challenge concerns the representation of a remarkable humpback whale song (Darewin - La Reunion), in order to help its analysis. Other special session concerns (neural)modelisation of the biosonar of bats or dolphins.

Glotin H, Dugan P, LeCun Y, Clark C, Halkias X, (2013) Proc. of the first workshop on Machine Learning for Bioacoustics, sabiod.org/ICML4B2013
proceedings.pdf, ICML4B

Glotin H, (2013) Etho-Acoustics: Categorisation & Localisation into Soundscapes, Ed. Intech open book

Pace F, Benard F, Glotin H, Adam O, White P, (2010) Subunit definition for humpback whale call classification, J. Applied Acoustics, 11(71)

Glotin H, Caudal F, Giraudet P, (2008) Whales cocktail party: a real-time tracking of multiple whales, V.36(1), ISSN 0711-6659, sabiod.org/oncet, J. Canadian Acoustics

Benard F, Glotin H, (2010) Automatic indexing and content analysis of whale recordings & XML representation, EURASIP Adv. Signal Proc. for Maritime Applications

Farabet C, Couprie C, Najman L, LeCun Y, (2013) Learning Hierarchical Features for Scene Labeling, IEEE PAMI

LeCun, Y, Learning Invariant Feature Hierarchies, (2012) Workshop on Biological & Computer Vision Interfaces, LNCS, V7583, ECCV

Anden J, Mallat S, (2011) Scattering transform applied to audio signals & musical classification: Multiscale Scattering for Audio Classification, ISMIR

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Halkias X, Ellis D, (2008) A Comparison of Pitch Extraction Methodologies for Dolphin Vocalizations, V36(1), J. Canadian Acoustics

Halkias X, Ellis D, (2006) Call Detection & Extraction Using Bayesian Inference, Special issue on Marine Mammal Detection, V67(11), J. Applied Acoustics

Halkias X, Paris S, Glotin H, (2013) Classification of mysticete sounds using machine learning techniques, 134, 3496, 10.1121/1.4821203, JASA

Author Information

Hervé GLOTIN (Univ Sud-Toulon & Inst. univ de France)
Yann LeCun (Facebook AI Research and New York University)

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University. He received the Electrical Engineer Diploma from ESIEE, Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits for computer perception.

Thierry Artières (LIF / AMU)
Stephane Mallat (Ecole Polytechnique Paris)

Stéphane Mallat received the Ph.D. degree in electrical engineering from the University of Pennsylvania, in 1988. He was then Professor at the Courant Institute of Mathematical Sciences. In 1995, he became Professor in Applied Mathematics at Ecole Polytechnique, Paris. From 2001 to 2007 he was co-founder and CEO of a semiconductor start-up company. In 2012 he joined the Computer Science Department of Ecole Normale Supérieure, in Paris. Stéphane Mallat’s research interests include signal processing, computer vision, harmonic analysis and learning. He wrote a “Wavelet tour of signal processing: the sparse way”. In 1997, he received the Outstanding Achievement Award from the SPIE Society and was a plenary lecturer at the International Congress of Mathematicians in 1998. He also received the 2004 European IST Grand prize, the 2004 INIST-CNRS prize for most cited French researcher in engineering and computer science, and the 2007 EADS grand prize of the French Academy of Sciences.

Ofer Tchernichovski (Hunter College, CUNY)
Xanadu Halkias (University of the South Toulon)

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