Loading...
Search for:
multivariate-laplace
0.007 seconds
Training-Based Speech Enhancement Using Non-Gaussian Distributions
, M.Sc. Thesis Sharif University of Technology ; Sameti, Hossein (Supervisor)
Abstract
Statistical approaches (purely statistical and model-based) are the most efficient methods in single-channel speech enhancement. Despite these efficiencies, the problem of speech enhancement is still a challenge. Recent researches which propose univariate non-Gaussian distributions are more appropriate for speech signal in different domains. Based on these univariate distributions, statistical approaches have been modified and consequently better results have been reported. The purpose of this thesis is speech enhancement based on hidden Markov model using multivariate non-Gaussian distribution. The results of speech enhancement algorithm based on hidden Markov model in DCT and DFT domains...
Hidden markov model-based speech enhancement using multivariate laplace and gaussian distributions
, Article IET Signal Processing ; Volume 9, Issue 2 , 2015 , Pages 177-185 ; 17519675 (ISSN) ; Veisi, H ; Sameti, H ; Sharif University of Technology
Institution of Engineering and Technology
2015
Abstract
In this paper, statistical speech enhancement using hidden Markov model (HMM) is studied and new techniques for applying non-Gaussian distributions are proposed. The superiority of using non-Gaussian distributions in online adaptive noise suppression algorithms has been proven; however, in this study, this approach is formulated in an HMM-based mean-square error estimator (MMSE) estimator in which a priori models are trained in an off-line manner. In addition, an analytical study of using different distributions other than autoregressive (AR) Gaussian distribution, such as Laplace, is presented in order to construct an accurate HMM as a priori model for discrete Fourier transform and...