Blind separation of instantaneous mixtures of dependent sources

Marc Castella, Pierre Comon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper deals with the problem of Blind Source Separation. Contrary to the vast majority of works, we do not assume the statistical independence between the sources and explicitly consider that they are dependent. We introduce three particular models of dependent sources and show that their cumulants have interesting properties. Based on these properties, we investigate the behaviour of classical Blind Source Separation algorithms when applied to these sources: depending on the source vector, the separation may be sucessful or some additionnal indeterminacies can be identified.

Original languageEnglish
Title of host publicationIndependent Component Analysis and Signal Separation - 7th International Conference, ICA 2007, Proceedings
PublisherSpringer Verlag
Pages9-16
Number of pages8
ISBN (Print)9783540744931
DOIs
Publication statusPublished - 1 Jan 2007
Externally publishedYes
Event7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007 - London, United Kingdom
Duration: 9 Sept 200712 Sept 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4666 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007
Country/TerritoryUnited Kingdom
CityLondon
Period9/09/0712/09/07

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