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Penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources

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journal contribution
posted on 11.01.2010, 12:38 by Wenwu Wang, Saeid Sanei, Jonathon Chambers
A new approach for convolutive blind source separation (BSS) by explicitly exploiting the second-order nonstationarity of signals and operating in the frequency domain is proposed. The algorithm accommodates a penalty function within the cross-power spectrum-based cost function and thereby converts the separation problem into a joint diagonalization problem with unconstrained optimization. This leads to a new member of the family of joint diagonalization criteria and a modification of the search direction of the gradient-based descent algorithm. Using this approach, not only can the degenerate solution induced by a unmixing matrix and the effect of large errors within the elements of covariance matrices at low-frequency bins be automatically removed, but in addition, a unifying view to joint diagonalization with unitary or nonunitary constraint is provided. Numerical experiments are presented to verify the performance of the new method, which show that a suitable penalty function may lead the algorithm to a faster convergence and a better performance for the separation of convolved speech signals, in particular, in terms of shape preservation and amplitude ambiguity reduction, as compared with the conventional second-order based algorithms for convolutive mixtures that exploit signal nonstationarity.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

WANG, W., SANEI, S. and CHAMBERS, J., 2005. Penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources. IEEE Transactions on Signal Processing, 53 (5), pp. 1654-1669

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2005

Notes

This is an article from the journal, IEEE Transactions on Signal Processing [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISSN

1053-587X

Language

en

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