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Accurate, Fast and Stable Denoising Source Separation Algorithms

  • Harri Valpola
  • Jaakko Särelä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)

Abstract

Denoising source separation is a recently introduced framework for building source separation algorithms around denoising procedures. Two developments are reported here. First, a new scheme for accelerating and stabilising convergence by controlling step sizes is introduced. Second, a novel signal-variance based denoising function is proposed. Estimates of variances of different source are whitened which actively promotes separation of sources. Experiments with artificial data and real magnetoencephalograms demonstrate that the developed algorithms are accurate, fast and stable.

Keywords

Independent Component Analysis Spectral Shift Source Separation Signal Subspace Source Estimate 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Harri Valpola
    • 1
    • 2
  • Jaakko Särelä
    • 2
  1. 1.Artificial Intelligence LaboratoryUniversity of ZurichZurichSwitzerland
  2. 2.Neural Networks Research CentreHelsinki University of TechnologyEspooFinland

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