自动化学报2017，Vol.43Issue(2)：248-258,11.DOI:10.16383/j.aas.2017.c150719
基于感知掩蔽深度神经网络的单通道语音增强方法
A Single-channel Speech Enhancement Approach Based on Perceptual Masking Deep Neural Network
摘要
Abstract
A new deep neural network (DNN) is proposed for single-channel speech enhancement,which incorporates the perceptual masking properties of psychoacoustic models.Firstly,the proposed DNN is trained to learn both the clean speech magnitude spectrum and the noise magnitude spectrum from the noisy magnitude spectrum.Secondly,the estimated clean speech magnitude spectrum is used to calculate the noise masking threshold.Then,the noise masking threshold and the estimated noise magnitude spectrum are combined to calculate a perceptual gain function.Finally,the enhanced speech magnitude spectrum are obtained by jointly training the perceptual gain function and the noisy speech magnitude spectrum.Experimental results on TIMIT with 20 noise types at various SNR (signal-noise ratio) levels demonstrate that the proposed perceptual masking DNN can effectively remove the noise while maintaining small speech distortion,so as to obtain better performance than the common DNN methods and the NMF (nonnegative matrix factorization) method,no matter noise conditions are included in the training set or not.关键词
语音增强/深度神经网络/感知增益函数/掩蔽阈值Key words
Speech enhancement/deep neural network/perceptual gain function/masking threshold引用本文复制引用
韩伟,张雄伟,闵刚,张启业..基于感知掩蔽深度神经网络的单通道语音增强方法[J].自动化学报,2017,43(2):248-258,11.基金项目
国家自然科学基金(61471394,61402519),江苏省自然科学基金(BK20140071,BK20140074)资助 Supported by National Natural Science Foundation of China (61471394,61402519),Natural Science Foundation of Jiangsu Province (BK20140071,BK20140074) （61471394,61402519）
