A Sparsity-Based Method to Solve Permutation Indeterminacy in Frequency-Domain Convolutive Blind Source Separation

被引：0

作者：

Sudhakar, Prasad ^{[1
]}

Gribonval, Remi ^{[1
]}

机构：

[1] Ctr Rech INRIA Rennes Bretagne Atlantique, Metiss Team, F-35042 Rennes, France

来源：

INDEPENDENT COMPONENT ANALYSIS AND SIGNAL SEPARATION, PROCEEDINGS | 2009年 / 5441卷

关键词：

Convolutive BSS; permutation ambiguity; sparsity; l(1)-minimization;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Existing methods for frequency-domain estimation of mixing filters in convolutive blind source separation (BSS) suffer from permutation and scaling indeterminacies in sub-bands. However, if the filters are assumed to be sparse in the time domain, it is shown in this paper that the l(1)-norm of the filter matrix increases as the sub-band coefficients are permuted. With this motivation, an algorithm is then presented which solves the source permutation indeterminacy, provided there is no scaling indeterminacy in sub-bands. The robustness of the algorithm to noise is also presented.

引用

页码：338 / 345

页数：8

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