We present and merge two speech/music classification approaches that we have developed. The first one is a differentiated modeling approach based on a spectral analysis, which is implemented using GMM (Gaussian mixture model). The other one is based on three original features: entropy modulation, stationary segment duration and number of segments. They are merged with the classical 4 Hertz modulation energy. Our classification system is a fusion of the two approaches. It is divided in two classifications (speech/non-speech and music/non-music) and provides 94% of accuracy for speech detection and 90% for music detection, with one second of input signal. Beside the spectral information and GMM, classically used in speech/music discrimination, simple parameters bring complementary and efficient information.
['Julien Pinquier', 'Jean-Luc Rouas', 'Régine André-Obrecht']
A fusion study in speech/music classification