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TASLP
2010

Speech Enhancement Using Gaussian Scale Mixture Models

9 years 1 months ago
Speech Enhancement Using Gaussian Scale Mixture Models
This paper presents a novel probabilistic approach to speech enhancement. Instead of a deterministic logarithmic relationship, we assume a probabilistic relationship between the frequency coefficients and the log-spectra. The speech model in the log-spectral domain is a Gaussian mixture model (GMM). The frequency coefficients obey a zero-mean Gaussian whose covariance equals to the exponential of the log-spectra. This results in a Gaussian scale mixture model (GSMM) for the speech signal in the frequency domain, since the log-spectra can be regarded as scaling factors. The probabilistic relation between frequency coefficients and log-spectra allows these to be treated as two random variables, both to be estimated from the noisy signals. Expectation-maximization (EM) was used to train the GSMM and Bayesian inference was used to compute the posterior signal distribution. Because exact inference of this full probabilistic model is computationally intractable, we developed two approaches t...
Jiucang Hao, Te-Won Lee, Terrence J. Sejnowski
Added 21 May 2011
Updated 21 May 2011
Type Journal
Year 2010
Where TASLP
Authors Jiucang Hao, Te-Won Lee, Terrence J. Sejnowski
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