Automated BSS-algorithm performance evaluation

  • Authors:

  • Marina Charwath;Imke Hahn;Sascha Hauke;Martin Pyka;Slawi Stesny;Dietmar Lammers;Steffen Wachenfeld;Markus Borschbach

  • Affiliations:

  • Dept. of Mathematics and Natural Science, Institute for Computer Science, University of Münster, Germany;Dept. of Mathematics and Natural Science, Institute for Computer Science, University of Münster, Germany;Dept. of Mathematics and Natural Science, Institute for Computer Science, University of Münster, Germany;Dept. of Mathematics and Natural Science, Institute for Computer Science, University of Münster, Germany;Dept. of Mathematics and Natural Science, Institute for Computer Science, University of Münster, Germany;Dept. of Mathematics and Natural Science, Institute for Computer Science, University of Münster, Germany;Dept. of Mathematics and Natural Science, Institute for Computer Science, University of Münster, Germany;Dept. of Mathematics and Natural Science, Institute for Computer Science, University of Münster, Germany and Distributed and Self-organizing Computer Systems, University of Chemnitz, Germany

  • Venue:
  • SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
  • Year:
  • 2006

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Abstract

A first step to perform a competition of methods for source separation is the development of a testsuite that supports the development and evaluation of blind source separation (BSS) algorithms in a highly automated way. The concept of our testsuite is presented and it is shown how the testsuite can be used to apply BSS-algorithms to four standard sub-problems. To compare the performance of arbitrary algorithms on given problems the testsuite allows the integration of new algorithms and testing problems using well defined interfaces. A brief example is given by the integration of the FlexICA, EVD, EVD24 and the FastICA algorithm and our results achieved from automated tests and parameter optimizations.