Title :
Noise classification using Gaussian Mixture Models
Author :
Gupta, Hitesh Anand ; Varma, Vinay M.
Author_Institution :
Birla Inst. of Technol., Electron. & Commun. Eng., Ranchi, India
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.
Keywords :
Gaussian noise; cepstral analysis; expectation-maximisation algorithm; speech processing; GMM; Gaussian mixture models; MFCC vector; acoustic signals; babble; car; energy coefficient; expectation maximization algorithm; mel-frequency cepstral coefficients; noise classification; noise data; noise sources; speech modelling; subway; threshold method; Accuracy; Feature extraction; Hidden Markov models; Signal to noise ratio; Speech; Training; Expectation Maximization; GMM; MFCC; Noise Classification; threshold method;
Conference_Titel :
Recent Advances in Information Technology (RAIT), 2012 1st International Conference on
Conference_Location :
Dhanbad
Print_ISBN :
978-1-4577-0694-3
DOI :
10.1109/RAIT.2012.6194530
