Adaptive semi-supervised tree SVM for sound event recognition in home environments

This paper addresses a problem in sound event recognition, more specifically for home environments in which training data is not readily available. Our proposed method is an extension of our previous method based on a robust semi-supervised Tree-SVM classifier. The key step in this paper is that the MFCC features are adapted using custom filters constructed at each classification node of the tree. This is shown to significantly improve the discriminative capability. Experimental results under realistic noisy environments demonstrate that our proposed framework outperforms conventional methods.