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

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

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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 null 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. © 2005 IEEE.

Item Type: Article
Authors :
Wang, W
Sanei, S
Chambers, JA
Date : May 2005
DOI : 10.1109/TSP.2005.845433
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:43
Last Modified : 31 Oct 2017 14:37

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