Browse by author
Lookup NU author(s): Pei Gao,
Dr Li Khor,
Dr Wai Lok Woo,
Professor Satnam Dlay
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
A novel polynomial-based neural network is proposed for nonlinear blind source separation. We focus our research on a recently presented mono-nonlinearity mixture where a linear mixing matrix is slotted into two mutually inverse nonlinearities. In this paper, we generalize the mono-nonlinearity mixing system to the situation where different nonlinearities are applied to the source signals. The theory of Series Reversion is merged with the neural network demixer to perform two layers of mutually inverse nonlinearities. The corresponding parameter learning algorithm for the proposed polynomial-based neural network demixer is also presented. Simulations have been carried out to verify the efficacy of the proposed approach. We demonstrate that the proposed network can successfully recover the original source signals in a blind mode under nonlinear mixing conditions. © 2006 IEEE.
Author(s): Gao P, Khor LC, Woo WL, Dlay SS
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Year of Conference: 2006
Number of Volumes: 5
Library holdings: Search Newcastle University Library for this item