Equivariant Adaptive Source Separation

Equivariant Adaptive Source Separation (1996)

by Jean-François Cardoso , Beate Laheld

Venue:	IEEE Trans. on Signal Processing
Citations:	325 - 7 self

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Datum	Value	Source
TITLE	Equivariant Adaptive Source Separation	user correction - Legacy Corrections
AUTHOR NAME	Jean-François Cardoso	user correction - Legacy Corrections
AUTHOR NAME	Beate Laheld	user correction - Legacy Corrections
ABSTRACT	Source separation consists in recovering a set of independent signals when only mixtures with unknown coefficients are observed. This paper introduces a class of adaptive algorithms for source separation which implements an adaptive version of equivariant estimation and is henceforth called EASI (Equivariant Adaptive Separation via Independence) . The EASI algorithms are based on the idea of serial updating: this specific form of matrix updates systematically yields algorithms with a simple, parallelizable structure, for both real and complex mixtures. Most importantly, the performance of an EASI algorithm does not depend on the mixing matrix. In particular, convergence rates, stability conditions and interference rejection levels depend only on the (normalized) distributions of the source signals. Close form expressions of these quantities are given via an asymptotic performance analysis. This is completed by some numerical experiments illustrating the effectiveness of the proposed ap...	user correction - Legacy Corrections
YEAR	1996	INFERENCE
VENUE	IEEE Trans. on Signal Processing	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	3017--3030	INFERENCE
VOLUME	44	INFERENCE
CITATIONS	25 found	ParsCit 1.0