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Exploiting Semantic Content for Singing Voice Detection

Abstract : In this paper we propose a method for singing voice detection in popular music recordings. The method is based on statistical learning of spectral features extracted from the audio tracks. In our method we use Mel Frequency Cepstrum Coefficients (MFCC) to train two Gaussian Mixture Models (GMM). Special attention is brought to our novel approach for smoothing the errors produced by the automatic classification by exploiting semantic content from the songs, which will significantly boost the overall performance of the system.
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Contributor : Jean-Luc Rouas Connect in order to contact the contributor
Submitted on : Monday, December 3, 2012 - 10:52:48 AM
Last modification on : Saturday, June 25, 2022 - 10:33:26 AM
Long-term archiving on: : Monday, March 4, 2013 - 3:45:55 AM


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  • HAL Id : hal-00759923, version 1



Leonidas Ioannidis, Jean-Luc Rouas. Exploiting Semantic Content for Singing Voice Detection. Sixth IEEE International Conference on Semantic Computing (IEEE ICSC2012), Sep 2012, Parlemo, Italy. ⟨hal-00759923⟩



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