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Journal Articles Signal Processing Year : 2017

Blind noisy mixture separation for independent/dependent sources through a regularized criterion on copulas

Abstract

The paper introduces a new method for Blind Source Separation (BSS) in noisy instantaneous mixtures of both independent or dependent source component signals. This approach is based on the minimization of a regularized criterion. Precisely, it consists in combining the total variation method for denoising with the Kullback–Leibler divergence between copula densities. The latter takes advantage of the copula to model the structure of the dependence between signal components. The obtained algorithm achieves separation in a noisy context where standard BSS methods fail. The efficiency and robustness of the proposed approach are illustrated by numerical simulations.
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Dates and versions

hal-01811736 , version 1 (11-06-2018)

Identifiers

Cite

M. El Rhabi, H. Fenniri, A. Ghazdali, A. Hakim, A. Keziou. Blind noisy mixture separation for independent/dependent sources through a regularized criterion on copulas. Signal Processing, 2017, 131, pp.502 - 513. ⟨10.1016/j.sigpro.2016.09.006⟩. ⟨hal-01811736⟩
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