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Peter Ahrendt

Improving music genre classification by short-time feature integration

Research output: Contribution to book/anthology/report/proceedingBook chapterResearchpeer-review

  • Anders Meng, Others, Denmark
  • P. Ahrendt
  • J. Larsen, Technical University of Denmark
Many different short-time features, using time windows of 10-30 ms, have been proposed for music segmentation, retrieval and genre classification. However, often the available time frame of the music to make the actual decision or comparison (the decision time horizon) is in the range of seconds instead of milliseconds. The problem of making new features on the larger time scale from the short-time features (feature integration) has received only little attention. The paper investigates different methods for feature integration and late information fusion for music genre classification. A new feature integration technique, the AR model, is proposed and seemingly outperforms the commonly used mean-variance features.
Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publication year1 Jan 2005
ISBN (print)9780780388741
Publication statusPublished - 1 Jan 2005
Externally publishedYes

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