General multilayer perceptron demixer scheme for nonlinear blind signal separation

W. L. Woo*, S. Sali

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

A new technique is presented for instantaneous blind signal separation from nonlinear mixtures using a general neural network based demixer scheme. The nonlinear demixer model follows directly from the general mixer model. In the first part of the paper a general mixer model is described which includes linear mixtures as a special case. In the second part the general framework for a demixer based on a feedforward multilayer perceptron (FMLP) employing a class of continuously differentiable nonlinear functions is presented. A detailed derivation of the learning algorithm used to adapt the demixer's parameters is given. Cost functions based on both maximum entropy (ME) and minimum mutual information (MMI) have been studied. The performance of the new technique was investigated using various experiments derived from the general mixer model and using real-time data. These studies illustrated the superiority and the generality of the new technique compared with existing methods.

Original languageEnglish
Pages (from-to)253-262
Number of pages10
JournalIEE Proceedings: Vision, Image and Signal Processing
Volume149
Issue number5
DOIs
Publication statusPublished - Oct 2002

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