reasonably well on both SC09 and ESC-50 for all metrics. Still, we find that all the evaluation metrics could not totally reflect human perception. For example, the STFT + Griffin-Lim process would seriously damage the SSIM score even when the input is simply ground truth (see the Griffin-Lim GT column); the perceptual distances are not affected by the process, but it may not totally reflect the amplitude (see Fig. \[fig:qualitative\]: GMCNN has low perceptual distance). On the other hand, although results from SimlarityGraph are quite natural to humans (since the mask is pasted with the other part of the sound clip), its performance is not as good in all metrics as the filled in contents are different. Surprisingly, image inpainting models GMCNN and DeepPrior outperform other baselines in all metrics (note that DeepPrior does not require training), whereas vocoders like WaveGlow and WaveRNN are not as good. It indicates that general image inpainting models could highly likely be adapted to handle spectrogram as well and our spectrogram-based model still have a large space to improve, such as the kernel size, training loss, etc. Also, though the perceptual loss we apply does help a little bit, it generally does not lead 