• Corpus ID: 209445167

Preprint-work in progress

@inproceedings{Xu2019PreprintworkIP,
  title={Preprint-work in progress},
  author={Chenliang Xu},
  year={2019}
}
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… 

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