Abstract
Gaussian Mixture Models (GMMs) have been proven effective in modeling speech and other acoustic signals. In this study, we have used GMMs to model different noise sources, viz. subway, babble, car and exhibition. Expectation maximization algorithm has been implemented to fit the model. Further, we present the ‘threshold’ method which uses the energy coefficient of the Mel - Frequency Cepstral Coefficients (MFCC) vector to determine the frames with noise (no speech) data.
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