Connectionists: [CFP] ICML uLearnBio 2014: int. wkp on Unsupervised Learning from Bioacoustic Big Data

[Herve Glotin]

[ Apologies for cross-posting - new call for paper - uLearnBio at ICML 2014]

- Int. Workshop on Unsupervised Learning from Bioacoustic Big Data -

joint to ICML 2014 - Int. Conference on Machine Learning - 25/26 June,
Beijing, China

Prog. Com.: Chamroukhi, Glotin, Dugan, Clark, Artières, LeCun

http://sabiod.univ-tln.fr/ulearnbio/


MAIN TOPICS:
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...)


IMPORTANT DATES (Extended):
13th April for regular paper from 2 to 6 pages,
or
30th may for keynote paper on one of the technical challenge.

All submissions will be reviewed by program committee members, and
will be 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 proceedings (with ISBN number), and
will be available online from the workshop website. The organizers
will discuss the opportunity of editing a special issue with a journal
and authors of the best quality submissions will be invited to submit
extended versions of their papers.


OBJECTIVES:
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
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 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.


CHALLENGES:
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


INVITED SPEAKERS:
Pr. G. McLachlan - Department of mathematics - University of Queensland, AU,
Dr. F. Chamroukhi - LSIS CNRS - Toulon Univ, FR,
Dr. P. Dugan - Bioacoustics Research Program on Big Data - Cornell Univ, USA.

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

Program Committee:
Dr. F. Chamroukhi - LSIS CNRS - Toulon Univ,
Pr. H. Glotin - LSIS CNRS - Institut Universitaire France - LSIS CNRS
- Univ. Toulon,
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 - NYU -
Facebook Research Center, NY.