Convolutive Underdetermined Source Separation through Weighted Interleaved ICA and Spatio-temporal Source Correlation

  • Francesco Nesta
  • Maurizio Omologo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7191)


This paper presents a novel method for underdetermined acoustic source separation of convolutive mixtures. Multiple complex-valued Independent Component Analysis adaptations jointly estimate the mixing matrix and the temporal activities of multiple sources in each frequency. A structure based on a recursive temporal weighting of the gradient enforces each ICA adaptation to estimate mixing parameters related to sources having a disjoint temporal activity. Permutation problem is reduced imposing a multiresolution spatio-temporal correlation of the narrow-band components. Finally, aligned mixing parameters are used to recover the sources through L 0-norm minimization and a post-processing based on a single channel Wiener filtering. Promising results obtained over a public dataset show that the proposed method is an effective solution to the underdetermined source separation problem.


Source Separation Blind Source Separation Nonnegative Matrix Factorization Acoustic Source Natural Gradient 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francesco Nesta
    • 1
  • Maurizio Omologo
    • 1
  1. 1.Center of Information TechnologyFondazione Bruno Kessler - IrstItaly

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