Blind Separation For Instantaneous Mixture of Speech Signals: Algorithms and Performances
Résumé
Because it can be found in many applications, the Blind Separation of Sources (BSS) problem has raised an increasing interest. According to the BSS, one should estimate some unknown signals (named sources) using multisensor output signals (i.e. observed or mixing signals). For the Blind Separation of Sources (BSS) problem, many algorithms have been proposed in the last decade. Most of these algorithms are based on High Order Statistics (HOS) criteria. In this paper, we focus on the blind separation of non-stationary signals (music, speech signal, etc) from their linear mixtures. At first, we present briefly the idea behind the separation of non-stationary sources using Second Order Statistics (SOS). After that, we introduce and compare three possible separating algorithms. Keywords: Decorrelation, Second order Statistics, Whiteness, Blind Separation of Sources, Natural Gradient, Kullback-Leibler Divergence, Hadamard Inequality, Jacobi Diagonalization, and Joint Diagonalization.