Toggle Main Menu Toggle Search

Open Access padlockePrints

Blind source separation of nonlinearly constrained mixed sources using polynomial series reversion

Lookup NU author(s): Pei Gao, Dr Li Khor, Dr Wai Lok Woo, Professor Satnam Dlay

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

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.


Publication metadata

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

Pages: V849-V852

ISSN: 1520-6149

Publisher: IEEE

URL: http://dx.doi.org/10.1109/ICASSP.2006.1661409

DOI: 10.1109/ICASSP.2006.1661409

Library holdings: Search Newcastle University Library for this item

ISBN: 142440469X


Share