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Type: Congresso
Title: A Necessary And Sufficient Condition For The Blind Extraction Of The Sparsest Source In Convolutive Mixtures
Author: Batany
Yves-Marie; Donno
Daniela; Duarte
Leonardo Tomazeli; Chauris
Herve; Deville
Yannick; Travassos Romano
Joao Marcos
Abstract: This paper addresses sparse component analysis, a powerful framework for blind source separation and extraction that is built upon the assumption that the sources of interest are sparse in a known domain. We propose and discuss a necessary and sufficient condition under which the l(0) pseudo-norm can be used as a contrast function in the blind source extraction problem in both instantaneous and convolutive mixing models, when the number of observations is at least equal to the number of sources. The obtained conditions allow us to relax the sparsity constraint of the sources to its maximum limit, with possibly overlapping sources. In particular, the W-disjoint orthogonality assumption of the sources can be discarded. Moreover, no assumption is done on the mixing system except invertibility. A differential evolution algorithm based on a smooth approximation of the l(0) pseudonorm is used to illustrate the benefits brought by our contribution.
Subject: Blind Source Separation
Blind Source Extraction
Convolutive Mixture
Sparse Component Analysis
L(0) Pseudo-norm
Editor: IEEE
New York
Citation: 2016 24th European Signal Processing Conference (eusipco). Ieee, p. 1628 - 1632, 2016.
Rights: fechado
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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