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A Biologically Motivated Solution to the Cocktail Party Problem

Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093-0407, U.S.A.

Syrus C. Nemat-Nasser

Department of Physics, University of California, San Diego, La Jolla, CA 92093-0319, U.S.A.

Rex Kerr

Department of Biology, University of California, San Diego, La Jolla, CA 92093-0349, U.S.A.

Raja Hayek

Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093-0407, U.S.A.

Christopher Downing

Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093-0407, U.S.A.

Robert Hecht-Nielsen

Department of Electrical and Computer Engineering, Program in Computational Neurobiology, Institute for Neural Computation, University of California, San Diego, La Jolla, CA 92093-0407, U.S.A., and HNC Software, San Diego, CA 92121, U.S.A.

Correspondence: bsagi@cerian.com

We present a new approach to the cocktail party problem that uses a cortronic artificial neural network architecture (Hecht-Nielsen, 1998) as the front end of a speech processing system. Our approach is novel in three important respects. First, our method assumes and exploits detailed knowledge of the signals we wish to attend to in the cocktail party environment. Second, our goal is to provide preprocessing in advance of a pattern recognition system rather than to separate one or more of the mixed sources explicitly. Third, the neural network model we employ is more biologically feasible than are most other approaches to the cocktail party problem. Although the focus here is on the cocktail party problem, the method presented in this study can be applied to other areas of information processing.

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