Perceptual improvement of deep neural networks for monaural speech enhancement

Monaural speech enhancement is a key yet challenging problem for many important real world applications. Recently, deep neural networks(DNNs)-based speech enhancement methods, which extract useful feature from complex feature, have demonstrated remarkable performance improvement. In this paper, we present a novel DNN architecture for monaural speech enhancement. Taking into account the masking properties of the human auditory system, a piecewise gain function is applied in the proposed DNN architecture, which is used to reduce the noise and make the residual noise perceptually inaudible. The proposed architecture jointly optimize the piecewise gain function and DNN. Systematic experiments on TIMIT corpus with 20 noise types at various signal-to-noise ratio (SNR) conditions demonstrate the superiority of the proposed DNN over the reference speech enhancement methods, no matter in the matched noise conditions or in the unmatched noise conditions.