A New Learning Algorithm for Blind Signal Separation

A new on-line learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of the sources. The Gram-Charlier expansion instead of the Edgeworth expansion is used in evaluating the MI. The natural gradient approach is used to minimize the MI. A novel activation function is proposed for the on-line learning algorithm which has an equivariant property and is easily implemented on a neural network like model. The validity of the new learning algorithm is verified by computer simulations. 3 Lab. for Information Representation, FRP, RIKEN, Wako-shi, Saitama, JAPAN 1 INTRODUCTION The problem of blind signal separation arises in many areas such as speech recognition, data communication, sensor signal processing, and medical science. Several neural network algorithms [3, 5, ...