Factors in automatic musical genre classification of audio signals

  • Li T
  • Tzanetakis G
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Abstract

Musical genres are categorical descriptions that are used to describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel set of features for representing rhythmic structure and strength is proposed. The performance of those feature sets has been evaluated by training statistical pattern recognition classifiers using real world audio collections. Based on the automatic hierarchical genre classification two graphical user interfaces for browsing and interacting with large audio collections have been developed.

Author-supplied keywords

  • Audio compression
  • Bandwidth
  • Computer science
  • Feature extraction
  • Hard disks
  • Humans
  • Machine learning
  • Music information retrieval
  • Peer to peer computing
  • Performance analysis

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Authors

  • Tao Li

  • G. Tzanetakis

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