NDLI: Speech enhancement based on hidden Markov model with discrete cosine transform coefficients using Laplace and Gaussian distributions

Content Provider	IEEE Xplore Digital Library
Author	Aroudi, A. Veisi, H. Sameti, H.
Copyright Year	2012
Description	Author affiliation: Department of Computer Engineering, Sharif University of Technology, Tehran-Iran (Aroudi, A.; Veisi, H.; Sameti, H.)
Abstract	This paper presents a novel HMM-based speech enhancement framework based on Laplace and Gaussian distributions in DCT domain. We propose analytical procedures for training clean speech and noise models with the aim of Baum's auxiliary function and present two MMSE estimators based on Gaussian-Gaussian (for clean speech and noise respectively) and Laplace-Gaussian combinations in the HMM framework. The performance evaluation is done using SNR and PESQ measures and the results of the proposed techniques are compared with AR-HMM approach. Higher SNR improvement is achieved for the proposed method in the Gaussian-Gaussian case in comparison with AR-HMM and Laplace-Gaussian techniques for both nonstationary and stationary noises. A similar result is obtained in term of PESQ in the presence of nonstationary noise types.
Starting Page	304
Ending Page	309
File Size	282760
Page Count	6
File Format	PDF
ISBN	9781467303811
e-ISBN	9781467303828
e-ISBN	9781467303804
DOI	10.1109/ISSPA.2012.6310565
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Speech Noise Discrete cosine transforms Speech enhancement Noise measurement Training
Content Type	Text
Resource Type	Article

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