Using Separate Losses for Speech and Noise in Mask-Based Speech Enhancement
Ziyi Xu, Samy Elshamy, Tim Fingscheidt
Abstract
Estimating time-frequency domain masks for speech enhancement using deep learning approaches has recently become a popular field in research. In this paper, we propose a novel components loss (CL) for the training of neural networks for speech enhancement. During the training process, the proposed CL offers separate control over suppression of the noise component and preservation of the speech component. We illustrate the potential of the proposed CL by example of a convolutional neural network (CNN) for mask-based speech enhancement. We show improvement in almost all employed instrumental quality metrics over the baseline losses, which comprises the conventional mean squared error (MSE) loss and also perceptual evaluation of speech quality (PESQ) loss. On average, more than 0.3 dB higher SNR improvement and an at least 0.1 points higher PESQ score on the speech component are obtained. In addition to that, a more naturally sounding residual noise and a consistently best PESQ on the enhanced speech is obtained. All improvements are more distinct at low SNR conditions.