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[Bioacoustic-monitoring] [lastCFP] ICML uLearnBio 2014: int. wkp on Unsu

To: Hervé Glotin <>, isis-all <>, Les Infos du Risc <>, Auditory list list <>, Liste MUSIC-IR <>, Bioacoustics-L <>, bioacoustic-monitoring <>, machinelistening <>, bull-i3 <>
Subject: [Bioacoustic-monitoring] [lastCFP] ICML uLearnBio 2014: int. wkp on Unsupervised Learning from Bioacoustic Big Data
From: Herve Glotin <>
Date: Tue, 15 Apr 2014 09:41:58 +0200
- Int'l Workshop on Unsupervised Learning from Bioacoustic Big Data -

joint to ICML 2014 - 25/26 June Beijing
Chamroukhi, Glotin, Dugan, Clark, Artières, LeCun
last deadline ext. = 25th April for regular paper (2 to 6 pages),

Unsupervised generative learning on big data,
Latent data models,
Model-based clustering,
Bayesian non-parametric clustering,
Bayesian sparse representation,
Feature learning,
Deep neural net,
Environmental scene analysis,
Big Bio-acoustic data structuration,
Species clustering (birds, whales...)

IMPORTANT DATES (last ext.):
25th April for regular paper (2 to 6 pages),
30th may for paper on one of the technical challenge.

All submissions will be reviewed by program committee, and assessed
based on their novelty, technical quality, potential impact, and
clarity of writing. All accepted papers will be published as part of
the ICMLUlb  workshop online proceedings with ISBN number. The
organizers discuss the opportunity of editing a special issue with a
journal, authors of the best quality submissions will be invited to
submit extended versions of their papers.

The general topic of uLearnBio is machine learning from bioacoustic
data, supervised method but also unsupervised feature learning and
clustering from bioacoustic data. 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
previous ICML and NIPS workshops on learning from bio-acoustic data
(ICML4B and NIPS4B books are available at

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 Antarctic (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 :

More information and open challenges =

Pr. G. McLachlan - Dept of mathematics - Univ. of Queensland, AU,
Dr. F. Chamroukhi - LSIS CNRS - Toulon Univ, FR,
Dr. P. Dugan - Bioacoustics R. Prog on Big Data - Cornell Univ, USA.

Program Committee:
Dr. F. Chamroukhi - LSIS CNRS - Toulon univ,
Pr. H. Glotin - LSIS CNRS - Inst. Univ 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 - Comp. Biological Learning Lab NYU & Facebook Research Center NY.

Herve'   Glotin,   Pr.
Institut Univ. de France (IUF) & Univ. Toulon (UTLN)
Head of information DYNamics & Integration (DYNI @ UMR CNRS LSIS)        
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