CFP: NIPS 2010 Workshop on Discrete Optimization in Machine Learning – Structures, Algorithms and Applications (DISCML)

Call for Papers

Discrete Optimization in Machine Learning
Structures, Algorithms and Applications

Workshop at the
24th Annual Conference on Neural Information Processing Systems
(NIPS 2010)

http://www.discml.cc

Submission Deadline: Friday October 29, 2010

Solving optimization problems with ultimately discretely solutions is
becoming increasingly important in machine learning: At the core of
statistical machine learning is to infer conclusions from data, and
when the variables underlying the data are discrete, both the tasks of
inferring the model from data, as well as performing predictions using
the estimated model are discrete optimization problems. This workshop
aims at exploring discrete structures relevant to machine learning and
techniques relevant to solving discrete learning problems. In addition to
studying discrete structures and algorithms, this year’s workshop will
put a particular emphasis on novel applications of discrete optimization
in machine learning.

We would like to encourage high quality submissions of short papers
relevant to the workshop topics. Accepted papers will be presented as
spotlight talks and posters. Of particular interest are new
algorithms with theoretical guarantees, as well as applications of
discrete optimization to machine learning problems in areas such as
the following:

Combinatorial algorithms
– Submodular & supermodular optimization
– Discrete convex analysis
– Pseudo-boolean optimization
– Randomized / approximation algorithms
Continuous relaxations
– Sparse approximation & compressive sensing
– Regularization techniques
– Structured sparsity models
Applications
– Graphical model inference & structure learning
– Clustering
– Feature selection, active learning & experimental design
– Structured prediction
– Novel discrete optimization problems in ML

Submission deadline: October 29, 2010

Length & Format: max. 6 pages NIPS 2010 format

Time & Location: December 11 2010, Whistler, Canada

Submission instructions: Email to submit(at)discml.cc

Organizers: Andreas Krause (California Institute of Technology),
Pradeep Ravikumar (University of Texas, Austin), Jeff A. Bilmes
(University of Washington), Stefanie Jegelka (Max Planck Institute
for Biological Cybernetics in Tuebingen, Germany)

NIPS 2010 Workshop: New Directions in Multiple Kernel Learning – Call for Contributions

CALL FOR PAPERS
New Directions in Multiple Kernel Learning
NIPS 2010 Workshop, Whistler, British Columbia, Canada
http://doc.ml.tu-berlin.de/mkl_workshop
— Submission Deadline: October 18, 2010 —

Research on Multiple Kernel Learning (MKL) has matured to the point
where efficient systems can be applied out of the box to various
application domains. In contrast to last year’s workshop, which
evaluated the achievements of MKL in the past decade, this workshop
looks beyond the standard setting and investigates new directions for
MKL.

In particular, we focus on two topics:
1. There are three research areas, which are closely related, but have
traditionally been treated separately: learning the kernel, learning
distance metrics, and learning the covariance function of a Gaussian
process. We therefore would like to bring together researchers from
these areas to find a unifying view, explore connections, and
exchange ideas.
2. We ask for novel contributions that take new directions, propose
innovative approaches, and take unconventional views. This includes
research, which goes beyond the limited classical sum-of-kernels
setup, finds new ways of combining kernels, or applies MKL in more
complex settings.

The workshop will include:
* A brief introduction talk
* 4 invited keynote talks on new views and directions in MKL
* 4 talks by authors of contributed papers
* A poster session of contributed papers, and a poster-spotlight
session
* A discussion panel

The organizing committee is seeking short research papers for
presentation at the workshop. The committee will select several
submitted papers for 15-minute talks and poster presentations. The
accepted papers will be published on the workshop web site.

We plan to publish proceedings of this workshop in a special issue of an
appropriate journal. We will submit a proposal for such an issue to the
Journal of Machine Learning Research.

Amongst others, we encourage submissions in the following areas:
* New views on MKL, e.g., from the perspectives of metric learning,
Gaussian processes, learning with similarity functions, etc.
* New approaches to MKL, in particular, kernel parameterizations
different than convex combinations and new objective functions
* Sparse vs. non-sparse regularization in similarity learning
* Use of MKL in unsupervised, semi-supervised, multi-task, and
transfer learning
* MKL with structured input/output
* Innovative applications

SUBMISSION GUIDELINES
Submissions should be written as extended abstracts, no longer than 4
pages in the NIPS latex style. Style files and formatting instructions
can be found at http://nips.cc/PaperInformation/StyleFiles. The
extended abstract may be accompanied by an unlimited appendix and
other supplementary material, with the understanding that anything
beyond 4 pages may be ignored by the program committee.

Please send your submission by email to
ml-newtrendsinmkl(at)lists.tu-berlin.de
before October 18. Notifications will be given on Nov 2. Topics that
were recently published or presented elsewhere are allowed, provided
that the extended abstract mentions this explicitly.

ORGANIZERS:
Marius Kloft (UC Berkeley), Ulrich Rueckert (UC Berkeley),
Cheng Soon Ong (ETH Zuerich), Alain Rakotomamonjy (University of
Rouen), Soeren Sonnenburg (TU Berlin/Max Planck FML), Francis Bach
(ENS/INRIA)

WORKSHOP HOMEPAGE:
http://doc.ml.tu-berlin.de/mkl_workshop

MLSB 2010, call for papers & registration open

Call for Posters

MLSB 2010

The Fourth International Workshop on Machine Learning in Systems Biology

15-16 October 2010, Edinburgh, Scotland

http://mlsb10.ijs.si/

**REGISTRATION IS NOW OPEN**

MOTIVATION

Molecular biology and all the biomedical sciences are undergoing a
true revolution as a result of the emergence and growing impact of a
series of new disciplines/tools sharing the “-omics” suffix in their
name. These include in particular genomics, transcriptomics,
proteomics and metabolomics, devoted respectively to the examination
of the entire systems of genes, transcripts, proteins and metabolites
present in a given cell or tissue type.

The availability of these new, highly effective tools for biological
exploration is dramatically changing the way one performs research in
at least two respects. First, the amount of available experimental
data is not a limiting factor any more; on the contrary, there is a
plethora of it. Given the research question, the challenge has
shifted towards identifying the relevant pieces of information and
making sense out of it (a “data mining” issue). Second, rather
than focus on components in isolation, we can now try to understand
how biological systems behave as a result of the integration and
interaction between the individual components that one can now monitor
simultaneously (so called “systems biology”).

Taking advantage of this wealth of “genomic” information has become a
conditio sine qua non for whoever ambitions to remain competitive in
molecular biology and in the biomedical sciences in general. Machine
learning naturally appears as one of the main drivers of progress in
this context, where most of the targets of interest deal with complex
structured objects: sequences, 2D and 3D structures or interaction
networks. At the same time bioinformatics and systems biology have
already induced significant new developments of general interest in
machine learning, for example in the context of learning with
structured data, graph inference, semi-supervised learning, system
identification, and novel combinations of optimization and learning
algorithms.

The Workshop is organized as “core – event” of Pattern Analysis,
Statistical Modelling and Computational Learning – Network of Excellence
2 (PASCAL 2, http://www.pascal-network.org/)

OBJECTIVE

The aim of this workshop is to contribute to the cross-fertilization
between the research in machine learning methods and their
applications to systems biology (i.e., complex biological and medical
questions) by bringing together method developers and
experimentalists. We encourage submissions bringing forward methods
for discovering complex structures (e.g. interaction networks,
molecule structures) and methods supporting genome-wide data analysis.

LOCATION AND CO-LOCATION

The workshop will take place 15-16 October 2010 at the Edinburgh
International Conference Centre and the Informatics Forum of the
University of Edinburgh. It will be part of the wokshop program of
ICSB 2010, The 11th International Conference on Systems Biology
(11-14 OCT 2010, http://www.icsb2010.org.uk/).

POSTER SUBMISSION INSTRUCTIONS

We invite you to submit an extended abstract of at least 1 page
describing new or recently published (2010) results, formatted
according to the Springer Lecture Notes in Computer Science
style. Each extended abstract must be submitted online via the Easychair
submission system: http://www.easychair.org/conferences/?conf=mlsb10

KEY DATES

Poster submission deadline: September 30th 2010

TOPICS

A non-exhaustive list of topics suitable for this workshop is given
below:

Methods

Machine learning algorithms
Bayesian methods
Data integration/fusion
Feature/subspace selection
Clustering
Biclustering/association rules
Kernel methods
Probabilistic inference
Structured output prediction
Systems identification
Graph inference, completion, smoothing
Semi-supervised learning

Applications

Sequence annotation
Gene expression and post-transcriptional regulation
Inference of gene regulation networks
Gene prediction and whole genome association studies
Metabolic pathway modeling
Signaling networks
Systems biology approaches to biomarker identification
Rational drug design methods
Metabolic reconstruction
Protein function and structure prediction
Protein-protein interaction networks
Synthetic biology

INVITED SPEAKERS (confirmed)

Florence d’Alche Buc, Universite d’Evry-Val d’Essonne, Evry, France
Nir Friedman, The Hebrew University of Jerusalem, Jerusalem, Israel
Ursula Kummer, BIOQUANT, University of Heidelberg, Germany
Hans Lehrach, Max Planck Institute for Molecular Genetics, Berlin, Germany
Vebjorn Ljosa, The Broad Institute of MIT and Harvard, USA

MLSB10 PROGRAM CHAIRS

Saöo Dûeroski, Jozef Stefan Institute, Ljubljana, Slovenia
Simon Rogers, University of Glasgow, UK
Guido Sanguinetti, University of Sheffield/University of Edinburgh, UK

Academic post at Edinburgh University: School of Informatics (in the area of Computer Vision): App Deadline Oct 29!!

RE: academic post in Design Informatics, including Computer Vision

The School of Informatics at The University of Edinburgh is inviting applications for a academic post in Design-Informatics, including areas of Computer Vision.
The post is advertised for a Lecturer (Assistant Professor) but more senior appointments may be considered. Relevant areas of desired expertise include (one or more of):

* Computer vision and other sensing systems, including embedded realtime vision systems.

* Interactive media, animation and augmented reality.

* Data transduction, in which designs give the ability to make sense of complex datasets through visualization, sound or haptic feedback.

* Exploration of design spaces, including those associated with computer systems (from microprocessors through operating systems to networks) and with data sets and data visualization.

* Design of software, including interface design and visualization but also including design-related aspects of the semantics and structure of software systems.

* Design for ubiquitous technologies, including social signaling and other systems for harnessing mass information through design of ubiquitous devices or monitoring of social interactions and social spaces.

* Cognition and communication in the process of design, including mental models and user-centred design.

The School of Informatics is one of the internationally leading research departments, covering most areas of artificial intelligence, cognitive science and computer science. The University of Edinburgh is consistently ranked by both the Times Higher Education Supplement and the Guardian Education Supplement as one of the world’s top universities.
Edinburgh is one of the most attractive and desirable living locations in the world.

Details of the post and the application procedure can be found at: http://www.jobs.ed.ac.uk/vacancies/index.cfm?fuseaction=vacancies.furtherdetails&vacancy_ref=3013456

Informal enquiries about the position can be made to any of:

Professor Sethu Vijayakumar
Telephone: +44 131 651 3444
Email: (sethu.vijayakumar(at)ed.ac.uk)

Professor David Robertson,
Head of School of Informatics
Telephone+44 131 650 2709
Email: (dr(at)inf.ed.ac.uk)

Professor Jon Oberlander
Telephone: +44 131 650 4439
Email: (jon(at)inf.ed.ac.uk).

Apply online at: www.jobs.ed.ac.uk.
Post Application Reference: 3013456
Closing date: 29 October 2010

NIPS workshop: Machine Learning for Next Generation Computer Vision Challenges

Call for contributions for a NIPS workshop: Machine Learning for Next Generation Computer Vision Challenges

————————————————————————————————————————————–

Website: http://sites.google.com/site/mlngcvc/

Submission deadline: 23:59, GMT, 18th October, 2010

Workshop overview:

————————–

This workshop seeks to excite and inform researchers to tackle the next level of problems in the area of Computer Vision. The idea is to both give Computer Vision researchers access to the latest Machine Learning research, and also to communicate to researchers in the machine learning community some of the latest challenges in computer vision, in order to stimulate the emergence of the next generation of learning techniques. The workshop itself is motivated from several different points of view:

1. There is a great interest in and take-up of machine learning techniques in the computer vision community. In top vision conferences such as CVPR, machine learning is prevalent: there is widespread use of Bayesian Techniques, Kernel Methods, Structured Prediction, Deep Learning, etc.; and many vision conferences have featured invited speakers from the machine learning community.
2. Despite the quality of this research and the significant adoption of machine learning techniques, often such techniques are used as “black box” parts of a pipeline, performing traditional tasks such as classification or feature selection, rather than fundamentally taking a learning approach to solving some of the unique problems arising in real-world vision applications.
3. Beyond object recognition and robot navigation, many interesting problems in computer vision are less well known. These include more complex tasks such as joint geometric/semantic scene parsing, object discovery, modeling of visual attributes, image aesthetics, etc.
4. Even within the domain of “classic” recognition systems, we also face significant challenges in scaling up machine learning techniques to millions of images and thousands of categories (consider for example the ImageNet data set).
5. Images often come with extra multi-modal information (social network graphs, user preference, implicit feedback indicators, etc) and this information is often poorly used, or integrated in an ad-hoc fashion.

This workshop therefore seeks to bring together machine learning and computer vision researchers to discuss these challenges, show current progress, highlight open questions and stimulate promising future research.

Call for Papers

——————

Papers are sought in the following areas:

* Use of multi-modal information in image tasks (e.g., text, GPS tags, timestamps, social network, implicit feedback, audio, user preferences)

* Image tasks beyond object classification — that is, novel applications (comprehensive scene understanding, object discovery, attribute learning, aesthetic analysis, modeling of the collective structure of large-scale image datasets, etc.)

* Novel learning techniques and features especially suited for the above applications

* Papers that emphasize on integrated learning approaches, in contrast to solving any issues purely via complex software engineering (i.e., by chaining standard methods).

* Methods that are truly scalable to millions of images and/or to large video repositories, which now dominate many vision tasks.

* Algorithms that really push the boundaries of Machine Learning for Computer Vision tasks, or applications which really push the boundaries of both disciplines are particularly sought.

The program committee will review papers and provide suggestions for either a poster or oral presentation. Note that scientific contribution is a must; however, we encourage preliminary approaches that partially solve a challenging issue, or solutions that target a problem of interest but are not necessarily state-of-the-art in terms of performance (e.g., a method that scales to 1 trillion images on a mobile phone, but is 2% behind the winner on the latest vision challenge so would not necessarily be considered ‘state of the art’). The aim of the workshop is to look to the future, as much as it is to demonstrate successes of the (recent) past.

Call for Demos/Projects

—————————–

We especially solicit posters and/or demos from projects (e.g. internal, NSF funded, EU projects). This can be from projects near completion — an opportunity to show the community what challenges were addressed and demonstrate and software / datasets / systems that were produced. Alternatively, these can form outlines, ideas, open problems. The idea is to raise awareness of all activities in the joint area of machine learning / computer vision among as many researchers as possible. We will aim to accommodate as many relevant demos/project posters as possible.

Our overall aim: is to promote fruitful discussion among researchers from both communities, to raise awareness of work / challenges / projects / datasets, and to provide a relaxed environment in which to discuss these aspects. We are not aiming at a processional mini-conference, the outcome of the workshop should be more than a list of papers to go and read: hopefully you will have new contacts and new research ideas to get very excited about.

Details:

———-

Website: http://sites.google.com/site/mlngcvc/

Submission deadline: 23:59, GMT, 18th October, 2010

CFP: Workshop on Social Behavior Analysis

Call for papers: Workshop on Social Behavior Analysis

Santa Barbara, CA , 24 or 25 March 2011 (This is a one day workshop, exact date will be announced soon), in conjunction with FG 2011

Important Dates

* Paper submission: 12 December 2010
* Notification to the authors: 13 January 2011
* Receipt of camera ready copy: 19 January, 2011

Webpage: http://www.idiap.ch/~oaran/sba/index.html

There is a strong interest in fields like computer vision, audio processing, multimedia, HCI, and pervasive computing, in designing computational models of human interaction in realistic social settings. Such interest is boosted by the increasing capacity to acquire behavioral data with cameras, microphones and other fixed and mobile sensors. Unlike the traditional HCI view, which emphasizes communication between a person and a computer, the emphasis of an emerging body of research has been shifting towards communicative social behavior in natural situations, with examples such as informal conversational settings, general workplace environments, interviews, and meeting scenarios.

The workshop will gather, discuss, and disseminate unpublished work on computational models and systems for the analysis of social behavior. Given the scope of Automatic Face and Gesture Recognition conference, we would like to focus on automatic techniques for visual analysis of human communication and on the applications that are built on top of it. We welcome contributions that present robust techniques for the analysis of gestures and facial expressions in natural conversational environments to model social behavior in everyday life and reason about them. We also strongly encourage the participation of colleagues from behavioral sciences: studies of nonverbal behavior and social interaction provide highly valuable information, concepts, and frameworks to guide automatic analysis, while efforts in automatic analysis of social behavior provide new tools, data, and insights to behavioral scientists interested in nonverbal behavior and social interaction.
We invite contributions that address the following (non-exhaustive) list of topics:

Social behavior analysis
* Analysis and recognition of visual social cues and others:
o Visual nonverbal cues (body postures, hand gestures, head gestures, actions …)
o Multimodal affect recognition
o Nonverbal cues from other sensors
* Multimodal computational models for the analysis, estimation, and prediction of social behavior aspects and dimensions (interest level, dominance, rapport, deception…) and of individual properties affecting it (e.g., personality traits, preferences…)
* Analysis of conversational dynamics
* Multimodal data corpora for social behavior analysis

Systems and devices for capturing social behavior
* Smart camera/microphone systems
* Novel sensor technologies
* Wearable devices
* Cell phones

Socially aware systems and applications
* Computers and robots in the human interaction loop
* Individual and group self-awareness
* Educational applications
* Workplace applications
* Healthcare applications
* Game applications
* Art & creative applications

Organizers:
Oya Aran, Idiap Research Institute
Daniel Gatica-Perez, Idiap Research Institute
Louis-Philippe Morency, University of Southern California
Fabio Pianesi, University of Trento

More information can be found on the workshop web site: http://www.idiap.ch/~oaran/sba/index.html

ML/DM Post doc Job Anouncement – at SYSTMOD – Univeristy of Liège

Dear Colleagues,

We open a post-doc position in machine learning at the University of Liège, Department of Electrical Engineering and Computer Science.

The candidate will work in the context of large scale machine learning and data mining, within the research unit of Systems and Modeling (see http://www.montefiore.ulg.ac.be/services/stochastic/new/doku.php) and in collaboration with PEPITe SA (see http://www.pepite.be/), in order to create innovative solutions for extracting predictive models from very high dimensional data in the form of time-series collected from complex dynamical processes.

The initial contract should start early January 2011, and will be of one year.

The ideal candidate should have a PhD in Machine Learning or Data Mining, and be interested by challenging real world applications.

To apply, please send your CV and motivation letter to Prof. Louis Wehenkel (L.Wehenkel(at)ulg.ac.be).

Best regards,

Louis Wehenkel

www.montefiore.ulg.ac.be/~lwh/

NIPS 2010 workshop: Learning on Cores, Clusters, and Clouds

Learning on Cores, Clusters, and Clouds
NIPS 2010 Workshop, Whistler, British Columbia, Canada

http://lccc.eecs.berkeley.edu/

— Submission Deadline: October 17, 2010 —

In the current era of web-scale datasets, high throughput biology, and
multilanguage machine translation, modern datasets no longer fit on a
single computer and traditional machine learning algorithms often have
prohibitively long running times. Parallel and distributed machine
learning is no longer a luxury; it has become a necessity. Moreover,
industry leaders have already declared that clouds are the future of
computing, and new computing platforms such as Microsoft’s Azure and
Amazon’s EC2 are bringing distributed computing to the masses.

The machine learning community is reacting to this trend in computing by developing new parallel and distributed machine learning techniques.
However, many important challenges remain unaddressed. Practical
distributed learning algorithms must deal with limited network
resources, node failures and nonuniform network latencies. In cloud
environments, where network latencies are especially large, distributed
learning algorithms should take advantage of asynchronous updates.

Many similar issues have been addressed in other fields, where
distributed computation is more mature, such as convex optimization and
numerical computation. We can learn from their successes and their
failures.

The one day workshop on “Learning on Cores, Clusters, and Clouds” aims
to bring together experts in the field and curious newcomers, to present
the state-of-the-art in applied and theoretical distributed learning,
and to map out the challenges ahead. The workshop will include invited
and contributed presentations from leaders in distributed learning and
adjacent fields.

We would like to invite short high-quality submissions on the following
topics:

* Distributed algorithms for online and batch learning
* Parallel (multicore) algorithms for online and batch learning
* Computational models and theoretical analysis of distributed and
parallel learning
* Communication avoiding algorithms
* Learning algorithms that are robust to hardware failures
* Experimental results and interesting applications

Interesting submissions in other relevant topics not listed above
are welcome too. Due to the time constraints, most accepted
submissions will be presented as poster spotlights.

_Submission guidelines:_

Submissions should be written as extended abstracts, no longer
than 4 pages in the NIPS latex style. NIPS style files and
formatting instructions can be found at
http://nips.cc/PaperInformation/StyleFiles. The submissions should
include the authors’ name and affiliation since the review process
will not be double blind. The extended abstract may be accompanied
by an unlimited appendix and other supplementary material, with
the understanding that anything beyond 4 pages may be ignored by
the program committee. Please send your submission by email to
submit.lccc(at)gmail.com before
October 17 at midnight PST. Notifications will be given on or
before Nov 7. Topics that were recently published or presented
elsewhere are allowed, provided that the extended abstract
mentions this explicitly; topics that were presented in
non-machine-learning conferences are especially encouraged.

_Organizers:_

Alekh Agarwal (UC Berkeley), Lawrence Cayton (MPI Tuebingen),
Ofer Dekel (Microsoft), John Duchi (UC Berkeley), John Langford
(Yahoo!)

_Program Committee:_

Ron Bekkerman (LinkedIn), Misha Bilenko (Microsoft), Ran
Gilad-Bachrach (Microsoft), Guy Lebanon (Georgia Tech), Ilan Lobel
(NYU), Gideon Mann (Google), Ryan McDonald (Google), Ohad Shamir
(Microsoft), Alex Smola (Yahoo!), S V N Vishwanathan (Purdue),
Martin Wainwright (UC Berkeley), Lin Xiao (Microsoft)

2nd TOBI Workshop: Call for Papers

TOBI Workshop II:

Translational issues in BCI development: user needs, ethics, and technology transfer

Fondazione Santa Lucia, Rome, Italy
Dec. 2-3, 2010

http://www.tobi-project.org/TOBI-workshop-2
——————————–

= Announcement =

The TOBI Project (Tools for Brain Interaction, http://www.tobi-project.org) is organizing its second workshop, which follows the one held in Graz on February 2010.

The goal of the 2nd TOBI workshop is to draw the current and future scenarios involving themes of utmost relevance to fill the gap between the promises of the neural engineering achievements and the clinical application reality in terms of BCIs as a daily use assisted device and as add-on intervention in the rehabilitation protocols:

i. user centered research and design
ii. neuroethics
iii. technology transfer

The scientific program will consist of keynote talks, oral presentations, poster presentations, and round table.

A satellite session on clustering of EU-funded projects will take place (open to participants to EU-funded projects).

Partial list of speakers:

– Richard Frackowiak, Centre Hospitalier Universitaire Vaudois, Switzerland.
– Andrea Kübler, Julius-Maximilians Universität Würzburg, Germany
– Donatella Mattia, Fondazione Santa Lucia, Rome, Italy
– José del R. Millán, Ecole Polytechnique Fédérale de Lausanne, Switzerland
– Klaus-R. Müller, Technische Universität Berlin, Germany
– Guglielmo Tamburrini, Federico II University of Naples, Italy
– Paul Timmers, Head of Unit for ICT for Inclusion in the European Commission (to be confirmed).

= Call for papers =

Participants are invited to submit a 2-pages paper, which will be peer reviewed. Template and instruction for submission can be found on the workshop’s web site.

Papers can be accepted either for an oral or a poster presentation.

Accepted papers will be published on a special issue on the International Journal of Bioelectromagnetism (ISSN 1456-7865).

Authors of selected papers will be invited to submit an extended version for a special issue to be published on a prominent journal of the field (to be announced).

Deadline for paper submission: October 11, 2010

= Registration =

Participants are required to register through the conference menagement system (link available on the workshop’s web site). Registration includes lunches, coffee breaks, and the social dinner.

Registration fees are:

70 Euro by November 1
120 Euro by November 19
170 Euro onsite

= Important dates =

Paper submission: 11 October 2010
Early registration: 1 November 2010
Late registration: 19 November 2010
Workshop: 2-3 December 2010

= Venue =

The workshop will be held at the Congress Center of Fondazione Santa Lucia IRCCS, via Ardeatina 306, in the south-eastern part of Rome, close to the Appia Antica Park.

Hotel rooms have been pre-booked downtown Rome (in the vicinity of Piazza Repubblica, conveniently linked the main city attractions by public transportation).

Complimentary buses will transfer the participants (at the beginning and at the end of the sessions) between the workshop venue and Piazza Repubblica.

The social program will include a visit to the archaeological attractions of the city, followed by a dinner downtown.
——————————–

= Info =

Official web page:

http://www.tobi-project.org/TOBI-workshop-2

Send info requests to:

tobiworkshop_AT_hsantalucia.it

Find a more detailed PDF version of the announcement at:

http://www.tobi-project.org/sites/default/files/public/Workshop/Announcement_2ndTOBIws.pdf

PASCAL CHiME Speech Separation and Recognition Challenge

We are pleased to announce the PASCAL ‘CHiME’ Speech Separation and Recognition Challenge.

In 2006 the PASCAL Network funded the first speech separation challenge, addressing the problem of separating and recognising speech artificially mixed with other speech. The best system was able to achieve super-human performance! We are now turning to a more realistic – but more difficult – scenario: recognising speech in the reverberant multisource mixtures that are typical of everyday listening conditions. Specifically, the challenge will employ binaural, distant-microphone recordings made over a period of several weeks in a real family house.

The challenge will be to separate and recognise simple command utterances which have been convolved with a binaural room impulse response and embedded in this continuous background. The challenge is motivated by the demands of real distant-microphone speech recognition applications and has been designed to draw participation from multiple disciplines including signal processing, computational hearing, machine learning and speech recognition. Evaluation will be through speech recognition results but participants will be allowed to submit either separated signals, robust speech features or the outputs of complete recognition systems. We are interested in measuring the performance of both emerging techniques and established approaches.

A full description of the challenge, including details of the source separation and recognition tasks, the noisy speech data sets, and the rules for participation can be found on the Challenge web site.

http://www.dcs.shef.ac.uk/spandh/chime/challenge.html

Results of the Challenge will be presented at a dedicated one-day workshop that will be held as a satellite event of Interspeech 2011 in Florence, Italy. Participants will be invited to submit abstracts or full papers for presentation at this event.

Schedule:

September 2010: Training and development data are available for download
October 2010: Additional tools are available
February 2011: Test data are released
31st March 2011: Submission deadline for the CHiME 2011 workshop.

If you have any questions please do not hesitate to contact the organisers, chime(at)dcs.shef.ac.uk

Best Regards,

Jon Barker, (University of Sheffield, UK)
Emmanuel Vincent, (INRIA Rennes, France)
Ning Ma, (University of Sheffield, UK)
Heidi Christensen, (University of Sheffield, UK)
Phil Green, (University of Sheffield, UK)