Towards efficient automated singer identification in large music databases

Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn 2006, Issue , 2006, Pages 59-66

  Shen, Jialie ^a   Cui, Bin ^b   Shepherd, John ^a   Tan, Kian Lee ^c  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b PEKING UNIVERSITY   (China)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Music Retrieval; Singer Identification

Indexed keywords

COMPUTER MUSIC; DATABASE SYSTEMS; ELECTRONIC DOCUMENT IDENTIFICATION SYSTEMS; FEATURE EXTRACTION; STATISTICAL METHODS; AUTOMATION; IDENTIFICATION (CONTROL SYSTEMS); INFORMATION RETRIEVAL SYSTEMS; WEB BROWSERS;

HYBRID SINGER IDENTIFIER (HSI); MUSIC RETRIEVAL; MUSIC SEGMENTS; SINGER IDENTIFICATION;

SPEECH RECOGNITION; COMPUTER MUSIC;

AUTOMATED SINGER IDENTIFICATION; HYBRID SINGER IDENTIFIER (HSI); MUSIC DATABASES; STATISTICAL MIXTURE MODELS;

EID: 33750344118     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1148170.1148184     Document Type: Conference Paper

References (9)

1. M. Bartsch and G. Wakefield. Singing voice identification using spectral envelop estimation. IEEE Trans. on Speech and Audio Processing, 12(2): 100-109, 2004.
2. C. Becchetti, L. Ricotti, and L. Ricotti. Speech Récognition. JohnWiley, 1999.
3. A. Berenzweig, D. P. W. Ellis, and S. Lawrence. Using voice segments to improve artist classification of music. In AES 22nd International Conference on Virtual, Synthetic and Entertainment Audio, pages 119-122, 2002.
4. A. L. Berenzweig and D. P. W. Ellis. Locating singing voice segments within music signals. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2001.
5. Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
6. Michael Collins, Robert E. Schapire, and Yoram Singer. Logistic regression, adaboost and bregman distances. In Proc. of the 13th Annual Conference on Learning Theory (COLT), pages 158-169, 2000.
7. Y. Freund and R. Schapire. A decision-theoretic generalzation of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997.
8. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Verlag, 2001.
9. Michael I. Jordan. Why the logistic function? a tutorial discussion on probabilities and neural networks. Technical Report 9503, MIT, 1995.