A new learning algorithm for blind signal separation-英文文献.pdf

A New Learning Algorithm for Blind Signal Separation s. Amari* A. Cichocki University of Tokyo Lab. for Artificial Brain Systems Bunkyo-ku, Tokyo 113, JAPAN FRP, RIKEN amari@sat.t.u- tokyo.ac.jp Wako-Shi, Saitama, 351-01, JAPAN cia@kamo.riken.go.jp H. H. Yang Lab. for Information Representation FRP, RIKEN Wako-Shi, Saitama, 351-01, JAPAN hhy@koala.riken .go.jp Abstract A new on-line learning algorithm which minimizes a statistical de- pendency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual in- formation (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 are verified by computer simulations. 1 INTRODUCTION The problem of blind signal separation arises in many areas such as speech recog- nition, data communication, sensor signal processing, and medical science. Several neural network algorithms [3, 5, 7] have been proposed for solving this problem.