Eliminating the negative effect of adverse environmental noise has been an intriguing and challenging task for speech technology. Neural networks (NNs)-based denoising techniques have achieved favorable performance in recent years. In particular, adding skip connections to NNs has been demonstrated to significantly improve the performance of NNs-based speech enhancement systems. However, in most of the studies, the adding of skip connections was kind of tricks of the trade and lack of sufficient analyses, quantitatively and/or qualitatively, on the underlying principle. This paper presents a denoising architecture of Convolutional Neural Network (CNN) with skip connections for speech enhancement. Particularly, to investigate the inherent mechanism of NNs with skip connections in learning the noise properties, CNN with different skip connection schemes are constructed and a set of denoising experiments, in which statistically different noises being tested, are presented to evaluate the performance of the denoising architectures. Results show that CNNs with skip connections provide better denoising ability than the baseline, i.e., the basic CNN, for both stationary and nonstationary noises. In particular, benefit by adding more sophisticated skip connections is more significant for nonstationary noises than stationary noises, which implies that the complex properties of noise can be learned by CNN with more skip connections.

消除不利的环境噪声的负面影响一直是语音技术的一个充满挑战的挑战。近年来，基于神经网络（NN）的去噪技术取得了良好的性能。特别是，已证明向NN添加跳过连接可显着提高基于NN的语音增强系统的性能。但是，在大多数研究中，添加跳过连接是一种交易技巧，并且缺乏对基本原理进行定量和/或定性分析的方法。本文提出了一种具有跳过连接的卷积神经网络（CNN）降噪架构，用于语音增强。特别地，为了研究具有跳过连接的NN的固有机制，以学习噪声属性，构造了具有不同跳过连接方案的CNN，并提出了一组降噪实验，其中测试了统计上不同的噪声，以评估降噪架构的性能。结果表明，对于平稳噪声和非平稳噪声，具有跳过连接的CNN均比基线（即基本CNN）具有更好的降噪能力。特别是，对于非平稳噪声，通过添加更复杂的跳过连接而获得的收益要比固定噪声更为重要，这意味着CNN可以通过使用更多跳过连接来学习噪声的复杂属性。结果表明，对于平稳噪声和非平稳噪声，具有跳过连接的CNN均比基线（即基本CNN）具有更好的降噪能力。特别是，对于非平稳噪声，通过添加更复杂的跳过连接而获得的收益要比固定噪声更为重要，这意味着CNN可以通过使用更多跳过连接来学习噪声的复杂属性。结果表明，对于平稳噪声和非平稳噪声，具有跳过连接的CNN均比基线（即基本CNN）具有更好的降噪能力。特别是，对于非平稳噪声，通过添加更复杂的跳过连接而获得的收益要比固定噪声更为重要，这意味着CNN可以通过使用更多跳过连接来学习噪声的复杂属性。