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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)

Abstract

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.

Keywords

Source Separation Blind Source Separation Nonnegative Matrix Factorization Acoustic Source Natural Gradient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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