Speech enhancement by Bayesian estimation of clean speech modeled as super Gaussian given a priori knowledge of phase
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- 作者：Sunnydayal Vanambathina ; sunny.conference@gmail.com" class="auth_mail" title="E-mail the corresponding author ; sunnydayal45@gmail.com" class="auth_mail" title="E-mail the corresponding author ; T. Kishore Kumar1 ; kishorefr@gmail.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：ML estimator ; MAP estimator ; MMSE estimator ; Laplace density ; von Mises distribution ; Nakagami distribution
- 刊名：Speech Communication
- 出版年：2016
- 出版时间：March 2016
- 年：2016
- 卷：77
- 期：Complete
- 页码：8-27
- 全文大小：1261 K
文摘
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Maximum Likelihood (ML), Maximum a posterior (MAP) and Minimum mean square error (MMSE) estimators are proposed for speech enhancement.
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Noise spectral amplitude priors assumed as Laplace, Gaussian probability density functions (pdf).
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Speech spectral amplitude priors as Nakagami, Gamma distributed.
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A joint MMSE estimate of the Complex speech coefficients given Uncertain Phase information is derived.