A Class Centric Feature and Classifier Ensemble Selection Approach for Music Genre Classification

  • Hasitha Bimsara Ariyaratne
  • Dengsheng Zhang
  • Guojun Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)


Music genre classification has attracted a lot of research interest due to the rapid growth of digital music. Despite the availability of a vast number of audio features and classification techniques, genre classification still remains a challenging task. In this work we propose a class centric feature and classifier ensemble selection method which deviates from the conventional practice of employing a single, or an ensemble of classifiers trained with a selected set of audio features. We adopt a binary decomposition technique to divide the multiclass problem into a set of binary problems which are then treated in a class specific manner. This differs from the traditional techniques which operate on the naive assumption that a specific set of features and/or classifiers can perform equally well in identifying all the classes. Experimental results obtained on a popular genre dataset and a newly created dataset suggest significant improvements over traditional techniques.


music retrieval feature selection classifier ensemble music genre classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hasitha Bimsara Ariyaratne
    • 1
  • Dengsheng Zhang
    • 1
  • Guojun Lu
    • 1
  1. 1.Gippsland School of ITMonash UniversityChurchillAustralia

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