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Blind source separation of postnonlinear convolutive mixture

Lookup NU author(s): Jingyi Zhang, Dr Wai Lok Woo, Professor Satnam Dlay

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Abstract

In this paper, a novel solution is developed to solve blind source separation of postnonlinear convolutive mixtures. The proposed model extends the conventional linear instantaneous mixture model to include both convolutive mixing and postnonlinear distortion. The maximum-likelihood (ML) approach solution based on the expectation-maximization (EM) algorithm is developed to estimate the source signals and the parameters in the proposed nonlinear model. In the proposed solution, the sufficient statistics associated with the source signals are estimated in the E-step, while the model parameters are optimized through these statistics in the M-step. However, the complication resulted from the postnonlinear function associated with the mixture renders these statistics difficult to be formulated in a closed form and hence causes intractability in the parameter optimization. A computationally efficient algorithm is proposed which uses the extended Kalman smoother (EKS) to facilitate the E-step tractable and a set of self-updated polynomials is used as the nonlinearity estimator to facilitate closed form estimations of the parameters in the M-step. The theoretical foundation of the proposed solution has been rigorously developed and discussed in details. Both simulations and recorded speech signals have been carried out to verify the success and efficacy of the proposed algorithm. Remarkable improvement has been obtained when compared with the existing algorithms.


Publication metadata

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

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Audio, Speech and Language Processing

Year: 2007

Volume: 15

Issue: 8

Pages: 2311-2330

Print publication date: 01/11/2007

ISSN (print): 1558-7916

ISSN (electronic): 1558-7924

Publisher: IEEE

URL: http://dx.doi.org/10.1109/TASL.2007.903310

DOI: 10.1109/TASL.2007.903310


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