A New Learning Algorithm for Blind Signal Separation

Part of Advances in Neural Information Processing Systems 8 (NIPS 1995)

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Shun-ichi Amari, Andrzej Cichocki, Howard Yang


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. 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.