Single Channel Speech Enhancement: MAP Estimation Using GGD Prior Under Blind Setup
نویسندگان
چکیده
This paper presents a statistical algorithm using Maximum A Posteri ori (MAP) estimation for the enhancement of single channel speech， contami nated by the additive noise， under the blind framework. The algorithm uses Generalized Gaussian Distribution (GGD) function as a prior probability to model magnitude of the Spectral Components (SC) of the speech and noise in the frequency domain. An estimation rule has been derived for the estimation of the SC of the clean speech signal under the presence of additive noise signal. Since the parsimony of the GGD distribution depends on its shape parameter， it provides flexible statistical model for the data with different distribution， e.g. impulsive， Laplacian， Gaussian， etc. The enhancement result for Laplacian noise have been presented and compared with that of the conventional Wiener filtering， which assumes Gaussian distribution f01" SCs of both the speech and nOIse.
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