A comparison of audio features for elementary sound based audio classification

In this paper we compare two sets of audio features in task of audio pattern searching based on elementary sound models. The rst set of features consist of well-known mel-frequency cepstral coefficients together with their rst and second order time derivatives. The second set was chosen from bag of features by particle swarm optimization algorithm and consist of following audio features: line spectral frequencies (LSF), spectral ux (SFX) and zero crossing rate (ZCR). Experimental results performed on AudioDat sound database show improvement of above 18.6 % of average F-measure when using the second selected combination of features.