Music playlist generation by assimilating GMMs into SOMs

  • Authors:
  • Wietse Balkema;Ferdi van der Heijden

  • Affiliations:
  • Corporate Research, Robert Bosch GmbH, Germany;Signals and Systems Group, Faculty Electrical Engineering, Mathematics and Computer Science, University of Twente, The Netherlands

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2010

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Abstract

A method for music playlist generation, using assimilated Gaussian mixture models (GMMs) in self organizing maps (SOMs) is presented. Traditionally, the neurons in a SOM are represented by vectors, but in this paper we propose to use GMMs instead. To this end, we introduce a method to adapt a GMM such that its distance to a second GMM decreases at a controllable rate. Self organization is demonstrated using a small music database and a music classification task.