Similarity Clustering of Music Files According to User Preference

  • Bastian Tenbergen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4827)


A plug-in for the Machine Learning Environment Yale has been developed that automatically structures digital music corpora into similarity clusters using a SOM on the basis of features that are extracted from files in a test corpus. Perceptionally similar music files are represented in the same cluster. A human user was asked to rate music files according to their subjective similarity. Compared to the user’s judgment, the system had a mean accuracy of 65.7%. The accuracy of the framework increases with the size of the music corpus to a maximum of 75%. The study at hand shows that it is possible to categorize music files into similarity clusters by taking solely mathematical features into account that have been extracted from the files themselves. This allows for a variety of different applications like lowering the search space in manual music comparison, or content-based music recommendation.


Feature Extraction Method Similarity Cluster Audio Data Test Corpus 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 2007

Authors and Affiliations

  • Bastian Tenbergen
    • 1
  1. 1.Human-Computer Interaction M.A. Program, State University of New York at Oswego, Oswego, NY, 13126, Formerly:, Cognitive Science Bachelor Program, School of Human Sciences, University of OsnabrückGermany

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