uLearnBio@ICML 2014: Workshop on Unsupervised Learning from Bioacoustic Big Data
joint to ICML 2014 - Int. Conference on Machine Learning - 25/26 June, Beijing, China
http://sabiod.univ-tln.fr/ulearnbio/

2nd call for paper - Main topics (not limited to):
Unsupervised generative learning on big data
Latent data models
Model-based clustering
Bayesian non-parametric clustering
Bayesian sparse representation
Feature learning
Deep neural net
Bioacoustics
Environmental scene analysis
Big Bio-acoustic data structuration
Species clustering (birds, whales...)

Deadlines :
13th April for regular paper,
or
30th may for keynote paper on one of the technical challenge.

The general topic of uLearnBio is machine learning from bioacoustic data, supervised method but also unsupervised feature learning and clustering from bioacoustic data. A special session will concern cluster analysis based on Bayesian Non-Parametrics (BNP), in particular the Infinite Gaussian Mixture Model (IGMM) formulation, Chinese Restaurant Process (CRP) mixtures and Dirichlet Process Mixtures (DPM).
The non-parametric alternative avoids assuming restricted functional forms and thus allows the complexity and accuracy of the inferred model to grow as more data is observed. It also represents an alternative to the difficult problem of model selection in model-based clustering models by inferring the number of clusters from the data as the learning proceeds.
ICMLulb offers an excellent framework to see how parametric and nonparametric probabilistic models for cluster analysis can perform to learn from complex real bio-acoustic data. Data issued from bird songs, whale songs, are provided in the framework of challenges as in our previous ICML and NIPS Workshops on learning from bio-acoustic data (ICML4B and NIPS4B books are available at http://sabiod.org ).

ICMLuLearnBio will bring ideas on how to proceed in understanding bioacoustics to provide methods for biodiversity indexing. The scaled bio-acoustic data science is a novel challenge for AI. Large cabled submarine acoustic observatory deployments permit data to be acquired continuously, over long time periods. For examples, submarine Neptune observatory in Canada, Antares or Nemo neutrino detectors, or PALAOA in Antartic (cf NIPS4B proc.) are 'big data' challenges. Automated analysis, including clustering/segmentation and structuration of acoustic signals, event detection, data mining and machine learning to discover relationships among data streams promise to aid scientists in discoveries in an otherwise overwhelming quantity of acoustic data.

In addition to the two previously announced challenges (Parisian bird and Whale challenges), we open a 3rd challenge on 500 amazonian bird species linked to the LifeClef Bird challenge 2014 but into an unsupervised way, over 9K .wav files.

Details on challenges : http://sabiod.univ-tln.fr/ulearnbio/challenges.html

Confirmed Invited Speakers:
Pr. G. McLachlan - Department of mathematics - University of Queensland, AU,
Dr. F. Chamroukhi - LSIS CNRS - Toulon Univ, FR,
Dr. P. Dugan - Ornithology Bioacoustics Lab - Cornell Univ, USA.

More information on open challenges = http://sabiod.org

Best regards, the organizers,
Dr. F. Chamroukhi - LSIS CNRS - Toulon Univ,
Pr. H. Glotin - LSIS CNRS - Institut Universitaire de France - Toulon Univ,
Dr. P. Dugan - Ornithology Bioacoustics Lab - Cornell Univ, NY,
Pr. C. Clark - Ornithology Bioacoustics Lab - Cornell Univ, NY,
Pr. T. Artières - LIP6 CNRS - Sorbonne Univ, Paris,
Pr. Y. LeCun - Computational & Biological Learning Lab - NY Univ - Facebook Research Center, NY.