Data Augmentation and Loss Normalization for Deep Noise Suppression

• Published in: Speech and Computer Vol. 12335; pp. 79 - 86
• Main Authors: Braun, Sebastian, Tashev, Ivan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Data augmentation; Deep noise suppression; Speech enhancement
• ISBN: 3030602753; 9783030602758
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-60276-5_8

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.