  Deep convolutional neural networks are known to specialize in distilling
compact and robust prior from a large amount of data. We are interested in
applying deep networks in the absence of training dataset. In this paper, we
introduce deep audio prior (DAP) which leverages the structure of a network and
the temporal information in a single audio file. Specifically, we demonstrate
that a randomly-initialized neural network can be used with carefully designed
audio prior to tackle challenging audio problems such as universal blind source
separation, interactive audio editing, audio texture synthesis, and audio
co-separation. To understand the robustness of the deep audio prior, we
construct a benchmark dataset \emph{Universal-150} for universal sound source
separation with a diverse set of sources. We show superior audio results than
previous work on both qualitative and quantitative evaluations. We also perform
thorough ablation study to validate our design choices.
