DocumentCode :
3282019
Title :
Singing voice detection of popular music using beat tracking and SVM classification
Author :
Fengyan Wu ; Shutao Sun ; Jianglong Zhang ; Yongbin Wang
Author_Institution :
Sch. of Comput. Sci., Commun. Univ. of China, Beijing, China
fYear :
2015
fDate :
June 28 2015-July 1 2015
Firstpage :
525
Lastpage :
528
Abstract :
Singing voice detection for musical structural analysis is an important but difficult area, which makes a great contribution to music segment. This paper proposes an efficient singing voice detection system by using beat tracking technique and SVM classification. We do the experiment using beat as a unit of classification instead of frame. Compared with a conventional frame-based classification, the run time of beat-based classification system reduces about 20%, and achieve 72.8% of precision. Meanwhile, in practice, the beat-based classification system achieves about 88.3% of precision.
Keywords :
music; signal classification; speech recognition; support vector machines; SVM classification; beat tracking; beat-based classification system; music segment; musical structural analysis; popular music; singing voice detection; Mel frequency cepstral coefficient; Production; Sun; Support vector machines; SVM; beat tracking; boundary detection; singing voice detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Information Science (ICIS), 2015 IEEE/ACIS 14th International Conference on
Conference_Location :
Las Vegas, NV
Type :
conf
DOI :
10.1109/ICIS.2015.7166648
Filename :
7166648
Link To Document :
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