Paper Info
Title
Bayesian STSA estimation using masking properties and generalized Gamma prior for speech enhancement
Abstract
We consider the estimation of the speech short-time spectral amplitude (STSA) using a parametric Bayesian cost function and speech prior distribution. First, new schemes are proposed for the estimation of the cost function parameters, using an initial estimate of the speech STSA along with the noise masking feature of the human auditory system. This information is further employed to derive a new technique for the gain flooring of the STSA estimator. Next, to achieve better compliance with the noisy speech in the estimator’s gain function, we take advantage of the generalized Gamma distribution in order to model the STSA prior and propose an SNR-based scheme for the estimation of its corresponding parameters. It is shown that in Bayesian STSA estimators, the exploitation of a rough STSA estimate in the parameter selection for the cost function and the speech prior leads to more efficient control on the gain function values. Performance evaluation in different noisy scenarios demonstrates the superiority of the proposed methods over the existing parametric STSA estimators in terms of the achieved noise reduction and introduced speech distortion.
Year
DOI
Venue
2015
10.1186/s13634-015-0270-6
EURASIP Journal on Advances in Signal Processing
Keywords
Field
DocType
Generalized Gamma distribution (GGD), Masking, Noise reduction, Short-time spectral amplitude (STSA), Speech enhancement
Speech enhancement,Noise reduction,Masking (art),Computer science,Speech recognition,Parametric statistics,Prior probability,Generalized gamma distribution,Bayesian probability,Estimator
Journal
Volume
Issue
ISSN
2015
1
1687-6180
Citations 
PageRank 
References 
0
0.34
18
Authors
4
Name
Order
Citations
PageRank
Mahdi Parchami152.77
Wei-Ping Zhu255562.46
Benoît Champagne351067.66
E. Plourde4375.92