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Penalty function approach for constrained convolutive blind source separation

Wang, W, Chambers, JA and Sanei, S (2004) Penalty function approach for constrained convolutive blind source separation In: ICA 2004: 5th International Conference, 2004-09-22 - 2004-09-24, Granada, Spain.

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A new approach for convolutive blind source separation (BSS) using penalty functions is proposed in this paper. Motivated by nonlinear programming techniques for the constrained optimization problem, it converts the convolutive BSS into a joint diagonalization problem with unconstrained optimization. Theoretical analyses together with numerical evaluations reveal that the proposed method not only improves the separation performance by significantly reducing the effect of large errors within the elements of covariance matrices at low frequency bins and removes the degenerate solution induced by a null unmixing matrix, but also provides an unified framework to constrained BSS. © Springer-Verlag 2004.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
Wang, W
Chambers, JA
Sanei, S
Date : 2004
DOI : 10.1007/978-3-540-30110-3_84
Contributors :
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:43
Last Modified : 31 Oct 2017 14:37

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