Automatic vocal segments detection in popular music
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Song, L; Li, M; Yan, Y
Published in: Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013
December 1, 2013
We propose a technique for the automatic vocal segments detection in an acoustical polyphonic music signal. We use a combination of several characteristics specific to singing voice as the feature and employ a Gaussian Mixture Model (GMM) classifier for vocal and non-vocal classification. We have employed a pre-processing of spectral whitening and archived a performance of 81.3% over the RWC popular music dataset. © 2013 IEEE.
Duke Scholars
Published In
Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013
DOI
Publication Date
December 1, 2013
Start / End Page
349 / 352
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Song, L., Li, M., & Yan, Y. (2013). Automatic vocal segments detection in popular music. In Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013 (pp. 349–352). https://doi.org/10.1109/CIS.2013.80
Song, L., M. Li, and Y. Yan. “Automatic vocal segments detection in popular music.” In Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013, 349–52, 2013. https://doi.org/10.1109/CIS.2013.80.
Song L, Li M, Yan Y. Automatic vocal segments detection in popular music. In: Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013. 2013. p. 349–52.
Song, L., et al. “Automatic vocal segments detection in popular music.” Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013, 2013, pp. 349–52. Scopus, doi:10.1109/CIS.2013.80.
Song L, Li M, Yan Y. Automatic vocal segments detection in popular music. Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013. 2013. p. 349–352.
Published In
Proceedings - 9th International Conference on Computational Intelligence and Security, CIS 2013
DOI
Publication Date
December 1, 2013
Start / End Page
349 / 352