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General multilayer perceptron demixer scheme for nonlinear blind signal separation

Lookup NU author(s): Dr Wai Lok Woo, Dr Sadettin Sali



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.

Publication metadata

Author(s): Woo WL, Sali S

Publication type: Article

Publication status: Published

Journal: IEE Proceedings: Vision, Image and Signal Processing

Year: 2002

Volume: 149

Issue: 5

Pages: 253-262

ISSN (print): 1350-245X

ISSN (electronic): 1359-7108

Publisher: IEEE


DOI: 10.1049/ip-vis:20020548


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