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

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.