Speech enhancement (SE) performance has improved considerably owing to the
use of deep learning models as a base function. Herein, we propose a perceptual
contrast stretching (PCS) approach to further improve SE performance. The PCS
is derived based on the critical band importance function and is applied to
modify the targets of the SE model. Specifically, the contrast of target
features is stretched based on perceptual importance, thereby improving the
overall SE performance. Compared with post-processing-based implementations,
incorporating PCS into the training phase preserves performance and reduces
online computation. Notably, PCS can be combined with different SE model
architectures and training criteria. Furthermore, PCS does not affect the
causality or convergence of SE model training. Experimental results on the
VoiceBank-DEMAND dataset show that the proposed method can achieve
state-of-the-art performance on both causal (PESQ score = 3.07) and noncausal
(PESQ score = 3.35) SE tasks.

本文提出了一种基于感知对比度拉伸 (PCS) 的方法来改善音频信号增强 (SE) 的性能，该方法通过对基于感知重要性的关键带重要性函数的目标特征的对比度进行拉伸，从而提高整体性能，并且可以与不同的 SE 模型架构和训练标准相结合，并且 PCS 不影响 SE 模型训练的因果性或收敛性，并可在 VoiceBank-DEMAND 数据集上实现最先进的性能。