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Robust and efficient content-based music retrieval system

  • Yuan-Shan Lee (a1), Yen-Lin Chiang (a1), Pei-Rung Lin (a1), Chang-Hung Lin (a1) and Tzu-Chiang Tai (a2)...

Abstract

This work proposes a query-by-singing (QBS) content-based music retrieval (CBMR) system that uses Approximate Karbunen–Loeve transform for noise reduction. The proposed QBS-CBMR system uses a music clip as a search key. First, a 51-dimensional matrix containing 39-Mel-frequency cepstral coefficients (MFCCs) features and 12-Chroma features are extracted from an input music clip. Next, adapted symbolic aggregate approximation (adapted SAX) is used to transform each dimension of features into a symbolic sequence. Each symbolic sequence corresponding to each dimension of MFCCs is then converted into a structure called advanced fast pattern index (AFPI) tree. The similarity between the query music clip and the songs in the database is evaluated by calculating a partial score for each AFPI tree. The final score is obtained by calculating the weighted sum of all partial scores, where the weighting of each partial score is determined by its entropy. Experimental results show that the proposed music retrieval system performs robustly and accurately with the entropy weighting mechanism.

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Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

Corresponding author: Tzu-Chiang Tai Email: tctai717@gmail.com

References

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Keywords

Robust and efficient content-based music retrieval system

  • Yuan-Shan Lee (a1), Yen-Lin Chiang (a1), Pei-Rung Lin (a1), Chang-Hung Lin (a1) and Tzu-Chiang Tai (a2)...

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