Sigma-delta learning for super-resolution independent component analysis

Many source separation algorithms fail to deliver robust performance in presence of artifacts introduced by cross-channel redundancy, non-homogeneous mixing and high-dimensionality of the input signal space. In this paper, we propose a novel framework that overcomes these limitations by integrating...

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Bibliographic Details
Published in2008 IEEE International Symposium on Circuits and Systems pp. 2997 - 3000
Main Authors Fazel, Amin, Chakrabartty, Shantanu
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.01.2008
Subjects
Analytical models
Delta-sigma modulation
Independent component analysis
Limit-cycles
Numerical simulation
Robustness
Signal processing
Signal resolution
Signal sampling
Source separation
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Summary:Many source separation algorithms fail to deliver robust performance in presence of artifacts introduced by cross-channel redundancy, non-homogeneous mixing and high-dimensionality of the input signal space. In this paper, we propose a novel framework that overcomes these limitations by integrating learning algorithms directly with the process of signal acquisition and sampling. At the core of the proposed approach is a novel regularized max-min optimization approach that yields "sigma-delta" limit-cycles. An on-line adaptation modulates the limit-cycles to enhance resolution in the signal subspaces containing non-redundant information. Numerical experiments simulating near-singular and non-homogeneous recording conditions demonstrate consistent improvements of the proposed algorithm over a benchmark when applied for independent component analysis (ICA).
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISBN:9781424416837
1424416833
ISSN:0271-4302
2158-1525
DOI:10.1109/ISCAS.2008.4542088