Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
I would like to ask about what I have observed when training with Denoiser model. When I train with the DAPS dataset, I notice a significant distortion in the results, but almost no distortion when using the DNS dataset. Is this because the DAPS dataset has very small and uniformly noise? Or is it related to the model structure itself?
Hallo there,
I would like to ask about what I have observed when training with Denoiser model. When I train with the DAPS dataset, I notice a significant distortion in the results, but almost no distortion when using the DNS dataset. Is this because the DAPS dataset has very small and uniformly noise? Or is it related to the model structure itself?
Hope to hear from you soon:)