Using wavelets and Gaussian Mixture Models for audio classification
In this paper, we present an audio classification system using wavelets for extracting low-level acoustic features. We perform multiple-level decomposition using Discrete Wavelet Transform to extract acoustic features at different scales and time from audio recordings. The extracted features are then translated into a compact vector representation. Gaussian Mixture Models with Expectation Maximization algorithm are then used to build models for sound classes. Specifically, three types of audio classification tasks are designed to evaluate the system, including speech/music classification, male/female speech classification, and music genre (classical, pop, jazz, and electronic) classification. By evaluating the system through 5-fold cross validation, the experimental result shows the promising capability of wavelets for speech and music analyses. © 2012 IEEE.
Proceedings - 2012 IEEE International Symposium on Multimedia, ISM 2012
Digital Object Identifier (DOI)
Chuan, Ching Hua; Vasana, Susan; and Asaithambi, Asai, "Using wavelets and Gaussian Mixture Models for audio classification" (2012). UNF Faculty Publications. 2829.