HMM-based noise estimator for speech enhancement  

作　　者：许春冬 夏日升 应冬文 李军锋 颜永红 

机构地区：[1]School of Information and Electronics,Beijing Institute of Technology [2]Faculty of Information Engineering,Jiangxi University of Science and Technology [3]Key Laboratory of Speech Acoustics and Content Understanding,Institute of Acoustics,Chinese Academy of Sciences

出　　处：《Journal of Beijing Institute of Technology》2014年第4期549-556,共8页北京理工大学学报（英文版）

基　　金：Supported by the National Key Basic Research Program of China(2013CB329302);the National Natural Science Foundation of China(61271426,10925419,90920302,61072124,11074275,11161140319,91120001);the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA06030100,XDA06030500);the National "863" Program(2012AA012503);the CAS Priority Deployment Project(KGZD-EW-103-2);Jiangxi Provincial Department of Education Science and Technology Project(GJJ13426)

摘　　要：A noise estimator was presented in this paper by modeling the log-power sequence with hidden Markov model （HMM）. The smoothing factor of this estimator was motivated by the speech presence probability at each frequency band. This HMM had a speech state and a nonspeech state, and each state consisted of a unique Gaussian function. The mean of the nonspeech state was the estimation of the noise logarithmic power. To make this estimator run in an on-line manner, an HMM parameter updated method was used based on a first-order recursive process. The noise signal was tracked together with the HMM to be sequentially updated. For the sake of reliability, some constraints were introduced to the HMM. The proposed algorithm was compared with the conventional ones such as minimum statistics （MS） and improved minima controlled recursive averaging （IM- CRA）. The experimental results confirms its promising performance.A noise estimator was presented in this paper by modeling the log-power sequence with hidden Markov model （HMM）. The smoothing factor of this estimator was motivated by the speech presence probability at each frequency band. This HMM had a speech state and a nonspeech state, and each state consisted of a unique Gaussian function. The mean of the nonspeech state was the estimation of the noise logarithmic power. To make this estimator run in an on-line manner, an HMM parameter updated method was used based on a first-order recursive process. The noise signal was tracked together with the HMM to be sequentially updated. For the sake of reliability, some constraints were introduced to the HMM. The proposed algorithm was compared with the conventional ones such as minimum statistics （MS） and improved minima controlled recursive averaging （IM- CRA）. The experimental results confirms its promising performance.

关 键 词：noise estimation hidden markov model CONSTRAINTS first-order recursive process speech enhancement 

分 类 号：TN912.3[电子电信—通信与信息系统]