Bio-inspired Audio Content-Based Retrieval Framework (B-ACRF)
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. Theoretica...
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/155/ http://eprints.utem.edu.my/id/eprint/155/1/Bio_Inspired_Audio_Content_Based_Retrieval_Framework_B-ACRF.pdf |
| Summary: | 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 |
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