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.

References:
Glotin H, Dugan P, LeCun Y, Clark C, Halkias X, (2013) Proc. of the first workshop on Machine Learning for Bioacoustics, sabiod.org/ICML4B2013proceedings.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

Lipkind D, Marcus GF...Tchernichovski O, (2013) Stepwise acquisition of vocal combinatorial capacity in songbirds & human infants, 10.1038/nature12173, Nature

Tchernichovski O, Wallman J, (2008) Neurons of imitation, 451(17), Nature

Lallemand I, Schwarz D, Artieres T, (2012) A Multiresolution Kernel Distance for SVM Classification of Environmental Sounds, SMC

Soullard Y, Artieres T, (2011) Hybrid HMM and HCRF model for sequence classification, ESANN

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