Bio-inspired audio content-based retrieval framework (B-ACRF)

Noor Azilah Draman Muda, Campbell Charles Wilson, Sea Ling

    Research output: Contribution to journalArticleOther


    Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.
    Original languageEnglish
    Pages (from-to)791-796
    Number of pages6
    JournalProceedings of the World Academy of Science, Engineering and Technology
    Publication statusPublished - 2009

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