  Long (> 200 ms) audio inpainting, to recover a long missing part in an audio
segment, could be widely applied to audio editing tasks and transmission loss
recovery. It is a very challenging problem due to the high dimensional, complex
and non-correlated audio features. While deep learning models have made
tremendous progress in image and video inpainting, audio inpainting did not
attract much attention. In this work, we take a pioneering step, exploring the
possibility of adapting deep learning frameworks from various domains inclusive
of audio synthesis and image inpainting for audio inpainting. Also, as the
first to systematically analyze factors affecting audio inpainting performance,
we explore how factors ranging from mask size, receptive field and audio
representation could affect the performance. We also set up a benchmark for
long audio inpainting. The code will be available on GitHub upon accepted.

Sound;Audio and Speech Processing