An information theoretic approach to blind source separation
Yogesh Pal Singh, C. S. Rai · Journal of Discrete Mathematical Sciences and Cryptography · 2001
Blind Source Separation (BSS) deals with separating independent signals from their linear mixtures observed at different sensors. In this paper a nonlinear function based on the cost function as Kullback-Leibler divergence between joint probability density function of the source vector and its parametric model has been proposed. This cost function is equivalent to maximization of information transfer between inputs and outputs and minimization of mutual information between the components of the output vector. Derivation process becomes extremely simple due to a simple approximation. Simulations with communication signals indicate that proposed algorithm provides better accuracy. The three most popular neural algorithms (EASI, natural gradient and Bell-Sejnowski algorithm) has been compared. Effectiveness of these algorithms depends upon the nonlinear activation function. These algorithms have been evaluated with different nonlinear functions for sub-Gaussian and super-Gaussian sources.