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Blind Source Separation via Unsupervised Learning

  • B. Freisleben
  • C. Hagen
  • M. Borschbach

Abstract

In this paper, a two-layer neural network is presented that organizes itself to perform blind source separation, i.e. it extracts the unknown independent source signals out of their linear mixtures. The convergence behaviour of the network is analyzed, and experimental results of separating historical speeches of four different speakers are presented.

Keywords

Learning Rule Independent Component Analysis Convergence Behaviour Blind Source Separation Linear Mixture 
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 Wien 1998

Authors and Affiliations

  • B. Freisleben
    • 1
  • C. Hagen
    • 2
  • M. Borschbach
    • 1
  1. 1.Department of Electrical Engineering and Computer Science (FB12)University of SiegenSiegenGermany
  2. 2.Department of Computer Science (FB20)University of DarmstadtDarmstadtGermany

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