【Title】Single channel speech enhancement via time-frequency dictionary learning

【Author】HUANG Jianjun ZHANG Xiongwei ZHANG Yafei ZOU Xia (Institute of Command Automation,PLA Univ.of Sci.&Tech.Nanjing 210007)

【Abstract】A time-frequency dictionary learning approach is proposed to enhance speech contaminated by additive nonstationary noise.In this framework,a time-frequency dictionary which is learned from noise data is incorporated into the convolutive nonnegative matrix factorization 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 indicate 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.