A New Learning Algorithm for Blind Signal Separation

A new on-line learning algorithm which minimizes a statistical de(cid:173) pendency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual in(cid:173) 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.