  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.
