Expectation-maximisation approach to blind source separation of nonlinear convolutive mixture

J. Zhang*, W. L. Woo, S. S. Dlay

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


A novel learning algorithm for blind source separation of post-nonlinear convolutive mixtures is proposed. The proposed mixture model characterises both convolutive mixture and post-nonlinear distortions of the sources. A novel iterative technique based on a maximum likelihood approach is developed where the expectation-maximisation (EM) algorithm is generalised to estimate the parameters in the proposed model. In the E-step of the proposed framework, sufficient statistics of the posterior distribution of the source signals are estimated while the model parameters are optimised through these statistics in the M-step. The post-nonlinear distortions, however, render these statistics difficult to express in a closed form, and hence, this causes intractability in the M-step. A computationally efficient algorithm is further proposed to facilitate the E-step tractable and the self-updated multilayer perceptron is developed in the M-step to estimate the nonlinearity. The theoretical foundation of the proposed solution has been rigorously developed and discussed in detail. Both simulations and real-time speech signals have been used to verify the success and efficacy of the proposed algorithm. Remarkable improvement has been obtained when compared with the existing algorithm.

Original languageEnglish
Pages (from-to)51-65
Number of pages15
JournalIET Signal Processing
Issue number2
Publication statusPublished - 25 Jun 2007


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