restoration and demonstrates a promising result. We also considers it as one of our baselines.

#### **Audio inpainting.**

is to fill gaps in audios, which has been extensively explored under different terminologies [@smaragdis2009missing; @wolfe2005interpolation]. Many work [@marafioti2018context; @bahat2015self] dedicates to gaps at the scale of several to tens of milliseconds that are due to packet loss in VoIP, clicks and impulsive noises. In these literature, gaps are at the scale of tens of milliseconds. For gaps ranging from hundreds of milliseconds to several seconds, [@bahat2015self] utilizes the statistics of recordings from the same user to perform inpainting and [@perraudin2018inpainting] proposes to use a graph to capture spectral similarity of different segments in the signal, where the most suitable one is used for inpainting. Nevertheless, [@perraudin2018inpainting] is only practical for signals with repeated patterns (e.g. music) and tends to fail on aperiodic signals like speech (cf. Table \[tab:benchmark\]), while [@bahat2015self] could only handle speech with the same identities. We still set [@perraudin2018inpainting] as one of the baselines since [@bahat2015self] is not suitable for datasets like ESC-50 [@piczak2015dataset] for audios inside are all natural sounds.

While there is also work on speech inpainting [@prablanc2016text], we do not compare with it as it 