Single channel speech enhancement via time-frequency dictionary learning  

作　　者：HUANG Jianjun ZHANG Xiongwei ZHANG Yafei ZOU Xia 

机构地区：[1]Institute of Command Automation,PLA Univ.of Sci.&Tech

出　　处：《Chinese Journal of Acoustics》2013年第1期90-102,共13页声学学报（英文版）

摘　　要：A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.A time-frequency dictionary learning approach is proposed to enhance speech con- taminated by additive nonstationary noise. In this framework, a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix fac- torization framework. The update rules for the time-varying gains and speech dictionary are derived by precomputing the noise dictionary. The magnitude spectra of speech are estimated using convolution operation between the learned speech dictionary and the time-varying gains. Finally, noise is removed via binary time-frequency masking. The experimental results indi- cate that the proposed scheme gives better enhancement results in terms of quality measures of speech. Moreover, the proposed algorithm outperforms the multiband spectra subtraction and the non-negative sparse coding based noise reduction algorithm in nonstationary noise conditions.

关 键 词：TIME WORK In STFT Single channel speech enhancement via time-frequency dictionary learning 

分 类 号：TN912.35[电子电信—通信与信息系统]