Missing feature kernel and nonparametric window subband power distribution for robust sound event classification

Abstract

Sound Event Classification (SEC) aims to understand the real life events using sound information. A major problem of SEC is that it has to deal with uncontrolled environmental conditions, leading to extremely high levels of noise, reverberation, overlapping, attenuation and distortion. As a result, some parts of the captured signals could be masked out or completely missing. In this paper, we propose a novel missing feature classification method by utilizing a missing feature kernel in the classification optimization machine. The proposed method first transforms audio segments into the Subband Power Distribution (SPD), a novel image representation where the pure signal’s area is separable. A novel masking approach is then proposed to separate the SPD into reliable and non-reliable parts. Next, missing feature kernel (MFK), in forms of probabilistic distances on the intersection between reliable areas of the SPD images, is developed and integrated into SVM optimization framework. Experimental results show superiority of the proposed method for challenging tasks of SEC, when signals come out with severe noises and distortions.