A New AR Model Based Speech Enhancement Approach Within Variational Bayesian Framework  

作　　者：HUANG Qinghua YANG Jie XUE Yunfeng 

机构地区：[1]Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, China

出　　处：《Chinese Journal of Electronics》2007年第3期499-502,共4页电子学报（英文版）

摘　　要：A new model-based speech enhancement algorithm by variational Bayesian learning was proposed in this paper. Autoregressive process was used to model speech signal and its order was determined automatically. Clean speech signal could be estimated using a variational Kalman smoother. Moreover, overfltting was avoided in the learning of model parameter and model structure. Experimental results compared with Kalman filter-based enhancement and spectral subtraction methods demonstrate the performance of our algorithm.

关 键 词：Speech enhancement Autoregressive model Variational Bayesian learning Variational Kalman smoother 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]