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Equivariant Adaptive Source Separation
 IEEE Trans. on Signal Processing
, 1996
"... 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 (Eq ..."
Abstract

Cited by 385 (10 self)
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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...
Performance And Implementation Of Invariant Source Separation Algorithms
 in ISCAS '96
, 1996
"... This paper focuses on the equivariant nature of source separation : the unknown parameter of source separation is an invertible matrix i.e. it belongs to a multiplicative group. In this instance, inference theory calls for `equivariant' estimation. This paper discusses some consequences of equi ..."
Abstract

Cited by 3 (0 self)
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This paper focuses on the equivariant nature of source separation : the unknown parameter of source separation is an invertible matrix i.e. it belongs to a multiplicative group. In this instance, inference theory calls for `equivariant' estimation. This paper discusses some consequences of equivariance with respect to implementation and performance of source separation algorithms. 1. SOURCE SEPARATION Source separation is receiving increasing attention in both signal processing and neural network literature since the seminal work of Jutten and H'erault [1]. The model of source separation is that of n statistically independent signals whose m (possibly noisy) linear combinations are observed; the problem consists in recovering the original signals from their mixture. The `blind' qualification refers to the coefficients of the mixture: no a priori information is assumed to be available about them. This feature makes the blind approach extremely versatile because it does not rely on mod...