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Penalty Function Approach for Constrained Convolutive Blind Source Separation

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

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.

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Author information

Authors and Affiliations

  1. Cardiff School of Engineering, Cardiff University, Queen’s Building, Cardiff, CF24 0YF, UK

    Wenwu Wang & Jonathon A. Chambers

  2. Division of Engineering, King’s College London, Strand, London, WC2R 2LS, UK

    Saeid Sanei

Authors
  1. Wenwu Wang
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  2. Jonathon A. Chambers
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  3. Saeid Sanei
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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Wang, W., Chambers, J.A., Sanei, S. (2004). Penalty Function Approach for Constrained Convolutive Blind Source Separation. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_84

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_84

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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