Abstract

Blind Signal Separation is the task of separating signals when only their mixtures are observed. Recently, Independent Component Analysis has become a favorite method of researchers for attacking this problem. We propose a new score function based on Generalized Laplace Distribution for the problem of blind signal separation for supergaussian and subgaussian. To estimate the parameters of such score function we used Nelder-Mead algorithm for optimizing the maximum likelihood function of Generalized Laplace Distribution. To blindly extract the independent source signals, we resort to FastICA approach. Simulation results show that the proposed approach is capable of separating mixture of signals.

Key words: Independent component analysis (ICA), generalized Laplace distribution (GLD), maximum likelihood (ML), Nelder-Mead (NM).