论文标题
深度噪声抑制的数据增强和丢失归一化
Data augmentation and loss normalization for deep noise suppression
论文作者
论文摘要
使用神经网络的语音增强，最近在研究中引起了广泛关注，并集成在商业设备和应用中。在这项工作中，我们调查了数据增强技术，以进行监督的基于深度学习的语音增强。我们表明，不仅将SNR值扩大到更广泛的范围，而且连续分布有助于正规化训练，而且还可以增强光谱和动态水平的多样性。但是，为了不通过级别增强降低训练，我们通过应用序列水平归一化提出对基于信号的损失函数的修改。我们在实验中表明，这种归一化克服了使用级别依赖性损耗函数在具有不平衡信号水平的序列上训练引起的降解。
Speech enhancement using neural networks is recently receiving large attention in research and being integrated in commercial devices and applications. In this work, we investigate data augmentation techniques for supervised deep learning-based speech enhancement. We show that not only augmenting SNR values to a broader range and a continuous distribution helps to regularize training, but also augmenting the spectral and dynamic level diversity. However, to not degrade training by level augmentation, we propose a modification to signal-based loss functions by applying sequence level normalization. We show in experiments that this normalization overcomes the degradation caused by training on sequences with imbalanced signal levels, when using a level-dependent loss function.
