milliseconds often take place during transmission where packets are subject to frequent events of loss due to unreliable communication channel. Lots of research has been dedicated to packet loss during transmission and had success tackling gaps at the scale of milliseconds. For small rates of lost data, sparsity-based [@adler2011audio; @siedenburg2013audio] sinusodial-based [@lagrange2005long], and autoregressive [@oudre2018interpolation] methods are proposed. And for situations with high packet loss rates in speech, [@bahat2015self] proposed using an example-based method that exploits prior information from the same user to fill in the gaps.

![Illustration of the long audio inpainting problem. Given a sound clip with part of it being masked out ($>$ 200 ms), the goal is to recover the masked part. Audio inpainting could be done on either raw waveform (left) or spectrogram (right). Long audio inpainting could be widely used for sound editing tasks such as swear words removal, music editing, etc.[]{data-label="fig:teaser"}](teaser_with_highlight.pdf){width="\linewidth"}

Larger gaps spanning for seconds could happen in various applications and cases, such as in music enhancement and restoration. [@perraudin2018inpainting] identifies the rather unrealistic assumption often made during shorter-range inpainting that the signal is stationary, which tends not to hold for longer gaps. They harness a similarity graph to obtain similarities between 