Two gradient descent algorithms for blind signal separation

Abstract

Two algorithms are derived based on the natural gradient of the mutual information of the linear transformed mixtures. These algorithms can be easily implemented on a neural network like system. Two performance functions are introduced based on the two approximation methods for evaluating the mutual information. These two functions depend only on the outputs and the de-mixing matrix. They are very useful in comparing the performance of different blind separation algorithms. The performance of the new algorithms is compared to that of some well known algorithms by using these performance functions. The new algorithms generally perform better because they minimize the mutual information directly. This is verified by the simulation results.