Open AccessLaplacian Speech Model and Soft Decision Based MMSE Estimator for Noise Power Spectral Density in Speech Enhancement
Author(s) -
Ou Shifeng,
Song Peng,
Gao Ying
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.09.009
Subject(s) - estimator , computer science , intelligibility (philosophy) , speech enhancement , noise (video) , minimum mean square error , noise power , spectral density , speech recognition , gaussian noise , gaussian , prior probability , noise reduction , mathematics , algorithm , power (physics) , artificial intelligence , statistics , physics , telecommunications , bayesian probability , philosophy , epistemology , quantum mechanics , image (mathematics)
The estimation of noise Power spectral density (PSD) is a very crucial issue for speech enhancement as a result of its significant effiect on the quality and intelligibility of the enhanced speech. Most of the existing estimators for noise PSD try to employ Gaussian speech priors, which, however, have been proven inconsistent with the reality. We derived an effiective solution to this problem of estimating noise PSD in the Minimum mean square error (MMSE) sense when the speech component is modeled by a Laplacian distribution. Meanwhile, the soft decision technique instead of the hard Voice activity detection (VAD) is evolved into our algorithm, which can automatically makes the estimation unbiased without requiring a bias compensation. The performance of the proposed method is tested by several objective and subjective measures under various stationary and nonstationary noise environments. The results confirm that our method achieves good performance for all the noise conditions and Signalnoise‐ratio (SNR) settings.