A signal subspace dimension estimator based on F-norm with application to subspace-based multi-channel speech enhancement  

作　　者：LI Chao LIU Wenju 

机构地区：[1]National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing 100190

出　　处：《Chinese Journal of Acoustics》2012年第3期353-368,共16页声学学报（英文版）

基　　金：supported by the National Nature Science Foundation of China(91120303,90820011, 90820303);the National Grand Fundamental Research 973 Program of China(2004CB318105)

摘　　要：Although the signal subspace approach has been studied extensively for speech enhancement, no good solution has been found to identify signal subspace dimension in multi- channel situation. This paper presents a signal subspace dimension estimator based on F-norm of correlation matrix, with which subspace-based multi-channel speech enhancement is robust to adverse acoustic environments such as room reverberation and low input signal to noise ratio （SNR）. Experiments demonstrate the presented method leads to more noise reduction and less speech distortion comparing with traditional methods.Although the signal subspace approach has been studied extensively for speech enhancement, no good solution has been found to identify signal subspace dimension in multi- channel situation. This paper presents a signal subspace dimension estimator based on F-norm of correlation matrix, with which subspace-based multi-channel speech enhancement is robust to adverse acoustic environments such as room reverberation and low input signal to noise ratio （SNR）. Experiments demonstrate the presented method leads to more noise reduction and less speech distortion comparing with traditional methods.

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TN911.7[自动化与计算机技术—计算机科学与技术]