Joint optimization of audible noise suppression and deep neural networks for single-channel speech enhancement
Wei Han, Xiongwei Zhang, Gang Min, Meng Sun, Jibin Yang · 2016
Improving the perceptual quality of speech signals is a key yet challenging problem for many real world applications. Taking into account the good performance of deep learning in signal representation, a novel single-channel speech enhancement technique is presented based on joint Deep Neural Networks and audible noise suppression as a whole network architecture. This new deep neural network jointly trains an audible noise suppression function which is used to estimate the magnitude spectrum of the clean speech and shape the spectrum of the audible noise at the same time. Experimental results on TIMIT with 20 noise types at various noise levels demonstrate the superiority of the proposed method over the baselines, no matter whether the noise conditions are included in the training set or not.