Exploiting Semantic Content for Singing Voice Detection

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.