Probabilistic distance SVM with Hellinger-Exponential Kernel for sound event classification

This paper presents a novel method for sound event classification based on probabilistic distance SVM. The basic idea is to embed probabilistic distances into classical SVM to classify the sound events. The main point of this method is that the long-term characterization of sound events are better used in the classification compared to conventional method. Furthermore, taking into account the relative short time span of sound events, we develop a probabilistic distance SVM approach based on Hellinger distance from exponential modeling of temporal subband envelopes. An experiment on classifying 10 types of sound events was carried out and showed promising results of the proposed method compared to conventional methods.