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Maximum likelihood approach to nonlinear convolutive blind source separation

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


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A novel learning algorithm for blind source separation of post-nonlinear convolutive mixtures with non-stationary sources is proposed in this paper. The proposed mixture model characterizes both convolutive mixture and post-nonlinear distortions of the sources. A novel iterative technique based on Maximum Likelihood (ML) approach is developed where the Expectation-Maximization (EM) algorithm is generalized to estimate the parameters in the proposed model. The post-nonlinear distortion is estimated by using a set of polynomials. The sufficient statistics associated with the source signals are estimated in the E-step while in the M-step, the parameters are optimized by using these statistics. In general, the nonlinear maximization in the M-step is difficult to be formulated in a closed form. However, the use of polynomial as the nonlinearity estimator facilitates the M-step tractable and can be solved via linear equations.

Publication metadata

Author(s): Zhang JY, Khor LC, Woo WL, Dlay SS

Editor(s): Rosca, J., Erdogmus, D., Principe, J.C., Haykin, S.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Independent Component Analysis and Blind Signal Separation: 6th International Conference (ICA)

Year of Conference: 2006

Pages: 926-933

ISSN: 0302-9743 (Print) 1611-3349 (Online)

Publisher: Springer


DOI: 10.1007/11679363_115

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783540326304