  Unsupervised deep learning methods for solving audio restoration problems
extensively rely on carefully tailored neural architectures that carry strong
inductive biases for defining priors in the time or spectral domain. In this
context, lot of recent success has been achieved with sophisticated
convolutional network constructions that recover audio signals in the spectral
domain. However, in practice, audio priors require careful engineering of the
convolutional kernels to be effective at solving ill-posed restoration tasks,
while also being easy to train. To this end, in this paper, we propose a new
U-Net based prior that does not impact either the network complexity or
convergence behavior of existing convolutional architectures, yet leads to
significantly improved restoration. In particular, we advocate the use of
carefully designed dilation schedules and dense connections in the U-Net
architecture to obtain powerful audio priors. Using empirical studies on
standard benchmarks and a variety of ill-posed restoration tasks, such as audio
denoising, in-painting and source separation, we demonstrate that our proposed
approach consistently outperforms widely adopted audio prior architectures.
