  Currently, most speech processing techniques use magnitude spectrograms as
front-end and are therefore by default discarding part of the signal: the
phase. In order to overcome this limitation, we propose an end-to-end learning
method for speech denoising based on Wavenet. The proposed model adaptation
retains Wavenet's powerful acoustic modeling capabilities, while significantly
reducing its time-complexity by eliminating its autoregressive nature.
Specifically, the model makes use of non-causal, dilated convolutions and
predicts target fields instead of a single target sample. The discriminative
adaptation of the model we propose, learns in a supervised fashion via
minimizing a regression loss. These modifications make the model highly
parallelizable during both training and inference. Both computational and
perceptual evaluations indicate that the proposed method is preferred to Wiener
filtering, a common method based on processing the magnitude spectrogram.
