An Environmental Sound Source Classification System Based on Mel-Frequency Cepstral Coefficients and Gaussian Mixture Models

Abstract

This paper proposed a study of a sound source classification system that has been developed for detecting and identifying the detected sound events in real environments. The proposed system was based on a pattern recognition approach using Gaussian mixture models and Mel-Frequency Cepstral Coefficients (MFCCs) features. We considered eight types of basic sound sources and an external sound. To make the system robust to various types of sound sources, we designed a tree of reference sound models for classification, in which especially generated total three of GMMs for external sounds according to different characteristics of frequency distributions. The performance of the proposed system, evaluated in terms of percent classification, indicated an averaged accuracy of 91.36% for off-line test. Finally, in on-line test our proposed system also showed a good and stable performance in real environments.