In the last few years, there have been many breakthroughs in the image processing world regarding repairing "damaged" images, images with corrupt pixels, or even reversing artefacts from formats such as JPEG, which have received media attention. I am trying to determine if these methodologies can similarly be used to predict future samples of audio, rather than pixels of an image.
much of which derives from an NVIDIA thesis published in EECV 2018: Image Inpainting for Irregular Holes Using Partial Convolutions, EECV 2018.
In theory, this basic method should have a direct analog to audio: where, rather than predicting missing pixels, we're trying to predict the next ms or so of future audio from the last second. So, it can really be thought of as a type of nonlinear prediction, which clearly performs very well.
QUESTION: What is the state of the art in using this method for audio processing rather than image processing? Is there any published literature on this? Does anyone have any references?
Below are some examples of the technique that I am talking about in the image processing realm. In all these situations, the missing pixel data is reconstructed from the remaining pixels, which is basically 2D sample prediction; I am hoping to get some insight into the 1D analog of this.