DocumentCode
1998393
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
fYear
2012
fDate
15-17 March 2012
Firstpage
821
Lastpage
825
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Recent Advances in Information Technology (RAIT), 2012 1st International Conference on
Conference_Location
Dhanbad
Print_ISBN
978-1-4577-0694-3
Type
conf
DOI
10.1109/RAIT.2012.6194530
Filename
6194530
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