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Optimization of Feature Processing Chain in Music Classification by Evolution Strategies

  • Igor Vatolkin
  • Wolfgang Theimer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

In this paper a new method based on evolution strategies (ES) is presented to optimize a classifier for personal music categories. The user assigns songs to multiple personal music categories: Examples from each category are selected in order to train a category-specific classifier using musical features as input. The classifier then ranks all songs according to their similarity to the category examples. Since an exhaustive search for parameters maximizing the classifier performance is not feasible an ES is applied. The experiments show a significant performance increase for various music categories due to the ES optimization.

Keywords

Audio Feature Evolution Strategy Tatum Time Partition Size Music Information Retrieval 
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 2008

Authors and Affiliations

  • Igor Vatolkin
    • 1
  • Wolfgang Theimer
    • 2
  1. 1.TU DortmundGermany
  2. 2.Research in MotionBochumGermany

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