Laplacian Speech Model and Soft Decision Based MMSE Estimator for Noise Power Spectral Density in Speech Enhancement

作     者：OU Shifeng SONG Peng GAO Ying 

作者机构：School of Science and Technology for Opto-electronic Information Yantai University School of Computer and Control Engineering Yantai University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2018年第27卷第6期

页      面：1214-1220页

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 07[理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 0701[理学-数学] 

基　　金：supported by the National Natural Science Foundation of China(No.61703360,No.61005021,No.61201457) the Natural Science Foundation of Shandong Province(No.ZR2017MF008,No.ZR2017MF019) 

主　　题：Noise PSD estimation Speech enhancement Laplacian speech model Soft decision 

摘      要：The estimation of noise Power spectral density(PSD) is a very crucial issue for speech enhancement as a result of its significant effect 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 effective 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.