S-TRANSFORM AND GAUSSIAN MIXTURE MODEL FOR ACOUSTIC SCENE CLASSIFICATION

Abstract

In this study, Acoustic Scene Classification (ASC) system is designed with the help of S-transform and Gaussian Mixture Model (GMM). The S-transform is an extension of continuous wavelet transform that combines the progressive resolution with phase information. Thus, it exhibits the amplitude response of the frequency samples in contrast to wavelet transform. The S-transform coefficients are modeled by GMM using posterior probabilities of testing features. Also, preprocessing of acoustic signals is done by a series of operations; explosion, pre-emphasis filtration and windowing approach. The number of Gaussian components which is used to model the scene is varied (GMM-4, GMM-8, GMM-16, and GMM-32) and the performance of ASC system is analyzed using TAU Urban Acoustic Scenes 2019. The results show the effectiveness of the system with average recognition rate of 77.59%, 81.58%, 87.66% and 84.50% for GMM-4, GMM-8, GMM-16, and GMM-32 respectively.