Robust and efficient content-based music retrieval system

APSIPA Transactions on Signal and Information Processing

Chang-Hung Lin²  Pei-Rung Lin²  Tzu-Chiang Tai¹  Yuan-Shan Lee²  Yen-Lin Chiang² 

[1] Providence University;National Central University

关键词: Query-by-singing;    Music retrieval;    Symbolic sequence;    Pattern indexing;    Information entropy;   

DOI  :  10.1017/ATSIP.2016.4

学科分类：计算机科学（综合）

来源: Cambridge University Press

【 摘 要 】

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.

【 授权许可】

Unknown