explored and promising results based on deep learning frameworks have been demonstrated on large mask inpainting, only a few papers [@marafioti2018context] have experimented deep learning on long audio inpainting, let alone discussing how different factors of a neural network could affect the inpainting performance.

Hence, in this work, we take a pioneering step toward long audio inpainting for editing purpose and beyond. As the first to explore the problem, we survey and experiment models from various domains such as image inpainting, Deep Image Prior [@ulyanov2018deep] and audio synthesis [@prenger2019waveglow]. We also propose two novel frameworks for unconstrained audio inpainting, where we systematically probe into how and to what extent various factors such as gap size, audio representation (either in waveform or spectrogram), receptive field and convolution type (dilated and gated convolution) could impact the inpainting performance, Also, we setup a benchmark for audio inpainting evaluation and hope it could facilitate future research in this domain. Our contributions could be summarized as follows:

-   We setup a benchmark for long audio inpainting and compare different baselines, based on SC09 dataset of human voice and ESC-50 dataset of natural sound.

-   We survey and evaluate the possibility of adapting models from different 