Jump function komogorov and its application for audio stream segmentation and classification

This paper proposes a new similarity measurement based on Jump Function Komogorov (JFK) and presents its application for audio content analysis. This is done by means of comparing JFK, a stochastic representation which is (a) additive, so a sum of sources yields a sum of JFK´s, and (b) sparse, so the signal and noise are better separated in the JFK domain. The properties of JFK make it more robust than the probability density function when comparing the signal distributions. In the application, we use the JFK in wavelet domain for the audio stream segmentation and classification. The experimental results show that the proposed method is comparable to the conventional methods under normal condition but significantly outperformed them under miss-match conditions.