Fast convergence polynomial based separation algorithm

L. C. Khor*, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nonlinear Blind Source Separation which is an extension of its more popular linear counterpart has gained increasing attention over recent years. Its development presents a more realistic approach due to the nonlinear mixing introduced by transmitter and receiver elements such as loudspeaker, amplifier and microphones. Though more accurate than linear models, it is also more complex and suffers from convergence issues. This paper proposes a polynomial neural network for blind nonlinear signal separation and also addresses the fundamental difficulty of non-unique solutions and slow convergence. Efficiency of the objective function is enhanced by a polynomial based model which is a flexible and more accurate fit. Coupled with reduced indeterminacy using additional constraints and improved convergence speed via adaptive learning rates, the proposed algorithm produces very promising results. Issues of convergence speed, accuracy and robustness against noise are investigated and results demonstrate the efficacy of the algorithm with adaptive learning rates.

Original languageEnglish
Title of host publicationProceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005
PublisherIEEE
Pages495-498
Number of pages4
Volume2
ISBN (Print)0780392434
DOIs
Publication statusPublished - 30 Jan 2006
Event8th International Symposium on Signal Processing and its Applications, ISSPA 2005 - Sydney, Australia
Duration: 28 Aug 200531 Aug 2005

Conference

Conference8th International Symposium on Signal Processing and its Applications, ISSPA 2005
CountryAustralia
CitySydney
Period28/08/0531/08/05

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