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Speech enhancement using neural networks is recently receiving largeattention in research and being integrated in commercial devices andapplications. In this work, we investigate data augmentation techniques forsupervised deep learning-based speech enhancement. We show that not onlyaugmenting SNR values to a broader range and a continuous distribution helps toregularize training, but also augmenting the spectral and dynamic leveldiversity. However, to not degrade training by level augmentation, we propose amodification to signal-based loss functions by applying sequence levelnormalization. We show in experiments that this normalization overcomes thedegradation caused by training on sequences with imbalanced signal levels, whenusing a level-dependent loss function.

Data augmentation and loss normalization for deep noise suppression