  Convolutional neural networks contain strong priors for generating natural
looking images [1]. These priors enable image denoising, super resolution, and
inpainting in an unsupervised manner. Previous attempts to demonstrate similar
ideas in audio, namely deep audio priors, (i) use hand picked architectures
such as harmonic convolutions, (ii) only work with spectrogram input, and (iii)
have been used mostly for eliminating Gaussian noise [2]. In this work we show
that existing SOTA architectures for audio source separation contain deep
priors even when working with the raw waveform. Deep priors can be discovered
by training a neural network to generate a single corrupted signal when given
white noise as input. A network with relevant deep priors is likely to generate
a cleaner version of the signal before converging on the corrupted signal. We
demonstrate this restoration effect with several corruptions: background noise,
reverberations, and a gap in the signal (audio inpainting).
