Networks for the Separation of Sources that are Superimposed and Delayed

Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)

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Authors

John Platt, Federico Faggin

Abstract

We have created new networks to unmix signals which have been mixed either with time delays or via filtering. We first show that a subset of the Herault-Jutten learning rules fulfills a principle of minimum output power. We then apply this principle to extensions of the Herault-Jutten network which have delays in the feedback path. Our networks perform well on real speech and music signals that have been mixed using time delays or filtering.