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Equivariant Adaptive Source Separation (1996)
| Venue: | IEEE Trans. on Signal Processing |
| Citations: | 449 - 9 self |
BibTeX
@ARTICLE{Cardoso96equivariantadaptive,
author = {Jean-François Cardoso and Beate Laheld},
title = {Equivariant Adaptive Source Separation},
journal = {IEEE Trans. on Signal Processing},
year = {1996},
volume = {44},
pages = {3017--3030}
}
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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...
Keyphrases
equivariant adaptive source separation source separation easi algorithm convergence rate equivariant estimation parallelizable structure complex mixture serial updating source signal mixing matrix asymptotic performance analysis interference rejection level specific form unknown coefficient equivariant adaptive separation close form expression stability condition adaptive algorithm adaptive version numerical experiment matrix update independent signal
