A new convolutive source separation approach for independent/dependent source components

被引：2

作者：

Mamouni, N. ^{[1
,4
]}

Keziou, A. ^{[1
]}

Fenniri, H. ^{[2
]}

Ghazdali, A. ^{[3
]}

Hakim, A. ^{[4
]}

机构：

[1] Univ Reims, LMR, UMR 9008, CNRS, Reims, France

[2] Univ Reims, CReSTIC, Reims, France

[3] Univ Sultan Moulay Slimane, ENSA Khouribga, LIPIM, Beni Mellal, Morocco

[4] Univ Cadi Ayyad, FST, LAMAI, Marrakech, Morocco

来源：

DIGITAL SIGNAL PROCESSING | 2020年 / 100卷

关键词：

Convolutive source separation; Dependent source components; Kullback-Leibler divergence between copula densities; Copula model selection; Statistical estimation; Gradient descent algorithm; BLIND SOURCE SEPARATION; SEMIPARAMETRIC ESTIMATION; ANALYSIS ALGORITHMS; MODEL; MIXTURES; TESTS;

D O I：

10.1016/j.dsp.2020.102701

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a new source separation approach, for linear convolutive mixtures of independent/dependent source components, is presented. It consists in minimizing an appropriate separation criterion, measuring the difference between the nonparametric copula density of the estimated sources and semiparametric copula densities modeling the dependency structure of the source components. The proposed approach represents an efficient tool for separating linear convolutive mixtures, especially, when the source components are statistically dependent, if prior information about the dependency structure of the source components is available. (C) 2020 Elsevier Inc. All rights reserved.

引用

页数：15

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