Improving Music Genre Classification by Short-Time Feature Integration

Anders Meng, Peter Ahrendt, Jan Larsen

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    Abstract

    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
    VolumeV
    Publication date2005
    Pages497-500
    Article number1416349
    ISBN (Print)0-7803-8874-7
    DOIs
    Publication statusPublished - 2005
    EventIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005) - Philadelphia, United States
    Duration: 18 Mar 200523 Mar 2005

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005)
    Country/TerritoryUnited States
    CityPhiladelphia
    Period18/03/200523/03/2005

    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

    Keywords

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

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