A survey of Sparse Component Analysis for blind source separation: principles, perspectives, and new challenges

Rémi Gribonval 1 Sylvain Lesage 1
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : In this survey, we highlight the appealing features and challenges of Sparse Component Analysis (SCA) for blind source separation (BSS). SCA is a simple yet powerful framework to separate several sources from few sensors, even when the independence assumption is dropped. So far, SCA has been most successfully applied when the sources can be represented sparsely in a given basis, but many other potential uses of SCA remain unexplored. Among other challenging perspectives, we discuss how SCA could be used to exploit both the spatial diversity corresponding to the mixing process and the morphological diversity between sources to unmix even underdetermined convolutive mixtures. This raises several challenges, including the design of both provably good and numerically efficient algorithms for large-scale sparse approximation with overcomplete signal dictionaries.
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Communication dans un congrès
ESANN'06 proceedings - 14th European Symposium on Artificial Neural Networks, Apr 2006, Bruges, Belgium. d-side publi., pp.323--330, 2006, 〈http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2006-157.pdf〉
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Contributeur : Rémi Gribonval <>
Soumis le : mardi 8 février 2011 - 22:03:39
Dernière modification le : mercredi 16 mai 2018 - 11:23:03
Document(s) archivé(s) le : lundi 9 mai 2011 - 02:49:36

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Rémi Gribonval, Sylvain Lesage. A survey of Sparse Component Analysis for blind source separation: principles, perspectives, and new challenges. ESANN'06 proceedings - 14th European Symposium on Artificial Neural Networks, Apr 2006, Bruges, Belgium. d-side publi., pp.323--330, 2006, 〈http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2006-157.pdf〉. 〈inria-00544897〉

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