  We study the use of the Wave-U-Net architecture for speech enhancement, a
model introduced by Stoller et al for the separation of music vocals and
accompaniment. This end-to-end learning method for audio source separation
operates directly in the time domain, permitting the integrated modelling of
phase information and being able to take large temporal contexts into account.
Our experiments show that the proposed method improves several metrics, namely
PESQ, CSIG, CBAK, COVL and SSNR, over the state-of-the-art with respect to the
speech enhancement task on the Voice Bank corpus (VCTK) dataset. We find that a
reduced number of hidden layers is sufficient for speech enhancement in
comparison to the original system designed for singing voice separation in
music. We see this initial result as an encouraging signal to further explore
speech enhancement in the time-domain, both as an end in itself and as a
pre-processing step to speech recognition systems.

Sound;Machine Learning;Neural and Evolutionary Computing;Audio and Speech Processing;Signal Processing