Improving Music Genre Classification by Short-Time Feature Integration

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2005



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Many different short-time features, using time windows in the size 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 only received little attention. This 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 publicationIEEE International Conference on Acoustics, Speech, and Signal Processing
Publication date2005
ISBN (print)0-7803-8874-7
StatePublished - 2005
EventIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005) - Philadelphia, Pennsylvania, United States


ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005)
CountryUnited States
CityPhiladelphia, Pennsylvania
Period23/03/2005 → …

Bibliographical note

Copyright: 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

CitationsWeb of Science® Times Cited: No match on DOI


  • Audio classification, early/late Information fusion, Feature Integration
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