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   NIPS 96 Workshop

Blind Signal Processing and Their Applications

(Neural Information Processing Approaches)

Saturday Dec 7, 1996
Snowmaas (Aspen), Colorado

Workshop Organizers:
Andrzej Cichocki
Andrew D. Back
Brain Information Processing Group
Frontier Research Program RIKEN,
The Institute of Physical and Chemical Research
Hirosawa 2-1, Wako-shi, Saitama, 351-01, Japan
Phone: +81-48-462-1111 ext: 6733
Fax: +81-48-462-4633

Blind Signal Processing is an emerging area of research in neural networks and image/signal processing with many potential applications. This field can be considered to have originated with blind deconvolution in the 70's followed by blind source separation in the mid 80's. Since then there has continued to be a strong and growing interest in the field. Blind signal processing problems can be classified into three areas: (1) blind signal separation of sources and/or independent component analysis (ICA), (2) blind channel identification and (3) blind deconvolution and blind equalization. These areas will be addressed in this workshop. See the objectives below for further details.




        The main objectives of this workshop are to:

Give presentations by experts in the field on the state of the art in this exciting area of research.

Compare the performance of recently developed adaptive un-supervised learning algorithms for neural networks.

Discuss issues surrounding prospective applications and the suitability of current neural network models. Hence we seek to provide a forum for better understanding current limitations of neural network models.

Examine issues surrounding local, online adaptive learning algorithms and their robustness and biologically plausibility or justification.

Discuss issues concerning effective computer simulation programs.

Discuss open problems and perspectives for future research in this area.

Especially, we intend to discuss the following items:

1. Criteria for blind separation and blind deconvolution problems (both for time and frequency domain approaches)

2. Natural (or relative) gradient approach to blind signal processing.

3. Neural networks for blind separation of time delayed and convolved signals.

4. On line adaptive learning algorithms for blind signal processing with variable learning rate (learning of learning rate).

5.Open problems, e.g. dynamic on-line determination of number of sources (more sources than sensors), influence of noise, robustness of algorithms, stability, convergence, identifiability, non-causal, non-stationary dynamic systems .

6. Applications in different areas of science and engineering, e.g., non-invasive medical diagnosis (EEG, ECG), telecommunication, voice recognition problems, image processing and enhancement.

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Workshop Schedule

A Review of Blind Signal Processing: Results and Open Issues
Andrzej Cichocki and Andrew Back
Brain Information Processing Group,
Frontier Research Program RIKEN, Japan


Natural Gradient in Blind Separation and Deconvolution - Information Geometrical Approach
Schun-ichi Amari
Brain Information Processing Group,
Frontier Research Program RIKEN, Japan

Abstract(zipped ps)

Entropic Contrasts for Blind Source Separation
Jean-Francois Cardoso
Ecole Nationale Superieure des Telecommunications, Paris, France

Abstract(zipped ps)

Coffee Break/Discussion Time
New Results On Blind Signal Separation: A General Information Theoretic Scheme With An Implementation Technique By Mixture Of Densities
Lei Xu. J. Ruan and Shun-ichi Amari
The Chinese University of Hong Kong, Hong Kong
Brain Information Processing Group, FRP, Riken, Japan


From Neural PCA to Neural ICA
Erkki Oja, Juha Karhunen and Aapo Hyvarinen
Helsinki University of Technology, Finland

Abstract(txt) , Paper(zipped ps)

Convergence Analysis of Local Algorithms for Blind Decorrelation
Scott Douglas and Andrzej Cichocki
Department of EE, University of Utah, USA
FRP Riken, Japan

Abstract(txt), Paper(zipped ps)

Negentropy and Kurtosis as Projection Pursuit Indices Provide Generalized ICA Algorithms
Mark Girolami and Colin Fyfe
The University of Paisley, Scotland

Paper(zipped ps)

Bussgang Methods for Separation of Multipath Mixtures
Russel Lambert
Dept of Electrical Engineering
University of South California, USA

Abstract(txt), Paper(zipped ps)

Discussion Time
Blind Signal Separation by Output Decorrelation
Dominic C.B. Chan, Simon J. Goodsil and Peter J.W. Rayner
University of Cambridge, United Kingdom

Abstract(ps), Paper(zipped ps)

Temporal Decorrelation Using Teacher Forcing Anti-Hebbian Learning and its Application in Adaptive Blind Source Separation
Jose C. Principe, Chuan Wang, and Hsiao-Chun Wu
University of Florida, USA


A Direct Adaptive Blind Equalizer for Multi-Channel Transmission
Seungjin Choi and Ruey-wen Liu
University of Notre Dame, USA

Abstract(txt), Paper(zipped ps)

Coffee Break/Discussion Time
IIR Filters for Blind Deconvolution Using Information Maximization
Kari Torkkola
Motorola Phoenix Corporate Research, USA

Abstract(txt), Paper(zipped ps), Slides(zipped ps)

Information Maximization and Independent Component Analysis: Is there a difference ?
D. Obradovic and G. Deco
Siemens AG,
Coporate Research and Development, Germany


Convergence Properties of Cichocki's Extension of the Herault-Jutten Source Separation Neural Network
Yannick Deville
Laboratoires d'Electronique Philips S.A.S. (LEP) France

Paper(zipped ps), Slides(zipped ps)

Independent Component Analysis of EEG and ERP Data
Tzyy-Ping Jung, Scott Makeig, Anthony J. Bell and Terrence J. Sejnowski
Computational Neurobiology Laboratory
The Salk Institute, CNL, USA

Abstract(zipped ps)

Blind separation of delayed and convolved sources - the problem
Tony Bell and Te-Won Lee
Computational Neurobiology Laboratory
The Salk Institute, CNL, USA


Information Back-propagation for Blind Separation of Sources from Non-linear Mixture
Howard H. Yang, Shun-ichi Amari and Andrzej Cichocki
Brain Information Processing Group, FRP, RIKEN, Japan

Abstract(txt), Paper(zipped ps)

Discussion Time

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Email Organizers  Last modified on 10 Mar 1997




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