domains for audio inpainting.

-   We designed novel waveform-based and spectrogram-based models for long audio inpainting.

-   We experimented different components for deep long audio inpainting, including kernel sizes and model layers.

Related Work
============

#### **Image and video inpainting.**

Inpainting models aim to restore the masked areas in the image/video, which could be widely used in image/video editing, such as object removal [@criminisi2003object; @chang2019vornet]. The masked areas are usually given, either by human annotation or segmentation models. The masked area could be a bounding box [@wang2018videoinp; @yu2018generative], an object [@Huang-SigAsia-2016] or in arbitrary shape [@yu2018free; @liu2018image; @chang2019free]. Many approaches have been proposed to address the inpainting problems, such as diffusion-based ones [@bertalmio2000image; @bertalmio2001navier] and patch-based ones [@barnes2009patchmatch; @Huang-SigAsia-2016]. In recent years, deep learning methods become dominant approaches for image inpainting [@yu2018free; @nazeri2019edgeconnect] and video inpainting [@kim2019deep; @chang2019learnable] due to the ability to recover unseen parts in an image based on learned data distribution during training. As a baseline, we fine-tune state-of-the-art image inpainting model [@wang2018image] to recover missing parts on spectrogram for audio inpainting.

Apart from trained deep image inpainting frameworks, Deep Image Prior [@ulyanov2018deep] offers a way to utilize the underlying structure in a untrained network for image 