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 |
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2009
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| 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 |
| _version_ | 1848886893144440832 |
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| author | Muda, N. A. |
| author_facet | Muda, N. A. |
| author_sort | Muda, N. A. |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-15T19:45:43Z |
| format | Conference or Workshop Item |
| id | utem-155 |
| institution | Universiti Teknikal Malaysia Melaka |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:45:43Z |
| publishDate | 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utem-1552015-05-28T02:17:02Z http://eprints.utem.edu.my/id/eprint/155/ Bio-inspired Audio Content-Based Retrieval Framework (B-ACRF) Muda, N. A. QA76 Computer software 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 2009-05 Conference or Workshop Item NonPeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/155/1/Bio_Inspired_Audio_Content_Based_Retrieval_Framework_B-ACRF.pdf Muda, N. A. (2009) Bio-inspired Audio Content-Based Retrieval Framework (B-ACRF). In: World Academy of Science Engineering and Technology, TOKYO, JAPAN. |
| spellingShingle | QA76 Computer software Muda, N. A. Bio-inspired Audio Content-Based Retrieval Framework (B-ACRF) |
| title | Bio-inspired Audio Content-Based Retrieval
Framework (B-ACRF) |
| title_full | Bio-inspired Audio Content-Based Retrieval
Framework (B-ACRF) |
| title_fullStr | Bio-inspired Audio Content-Based Retrieval
Framework (B-ACRF) |
| title_full_unstemmed | Bio-inspired Audio Content-Based Retrieval
Framework (B-ACRF) |
| title_short | Bio-inspired Audio Content-Based Retrieval
Framework (B-ACRF) |
| title_sort | bio-inspired audio content-based retrieval
framework (b-acrf) |
| topic | QA76 Computer software |
| url | 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 |