NDLI: Nonnegative HMM for Babble Noise Derived From Speech HMM: Application to Speech Enhancement

Content Provider	IEEE Xplore Digital Library
Author	Mohammadiha, N. Leijon, A.
Copyright Year	2006
Abstract	Deriving a good model for multitalker babble noise can facilitate different speech processing algorithms, e.g., noise reduction, to reduce the so-called cocktail party difficulty. In the available systems, the fact that the babble waveform is generated as a sum of N different speech waveforms is not exploited explicitly. In this paper, first we develop a gamma hidden Markov model for power spectra of the speech signal, and then formulate it as a sparse nonnegative matrix factorization (NMF). Second, the sparse NMF is extended by relaxing the sparsity constraint, and a novel model for babble noise (gamma nonnegative HMM) is proposed in which the babble basis matrix is the same as the speech basis matrix, and only the activation factors (weights) of the basis vectors are different for the two signals over time. Finally, a noise reduction algorithm is proposed using the derived speech and babble models. All of the stationary model parameters are estimated using the expectation-maximization (EM) algorithm, whereas the time-varying parameters, i.e., the gain parameters of speech and babble signals, are estimated using a recursive EM algorithm. The objective and subjective listening evaluations show that the proposed babble model and the final noise reduction algorithm significantly outperform the conventional methods.
Sponsorship	IEEE Signal Processing Society
Starting Page	998
Ending Page	1011
Page Count	14
File Size	3258968
File Format	PDF
ISSN	15587916
Volume Number	21
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Speech Noise Speech enhancement Vectors Noise reduction speech enhancement Babble noise hidden Markov model nonnegative matrix factorization
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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