Acoustic events detection using MFCC and MPEG-7 descriptors

Abstract

This paper is focused on the acoustic events detection. Particularly two types of acoustic events (gun shot, breaking glass) were investigated. For any detection task the feature extraction methods play very important role. The feature extraction influences the recognition rate, therefore it is most important in any pattern recognition task. In this paper the impact of Mel-Frequency Cepstral Coefficients - MFCC and selected set of MPEG-7 low-level descriptors were examined. The best feature set contained MFCC and selected descriptors such as ASC, ASS, ASF. They were used to represent the sounds of acoustic events and background. We obtained the improvement of the detection rate using the mentioned set of features. In this task GMM classifiers are used to model the sound classes. This paper describes a basic aspect of our work. © 2011 Springer-Verlag.