Jump Function Kolmogorov for overlapping audio event classification

This paper presents a novel method for audio event classification in overlapping conditions. The method is based on Jump Function Kolmogorov (JFK), a stochastic representation, which is (a) additive, thus the sum of signal and noise yields the sum of their JFKs; (b) sparse, therefore audio events are separable in this domain. The proposed method is an extension of our previous works for classification under noise-mismatch conditions. Similar to that approach, the robustness of the JFK feature is obtained by limiting them within confidence intervals, which can be learned in advance. However, in order to classify overlapped events, we design the classification system as a set of event detectors and develop a novel approach which maps JFKs to a specific feature for each detector. The experiment shows that the proposed method achieves promising results in very challenging overlapping conditions.