to large improvement.

Qualitative Results
-------------------

We compare output spectrograms from different baselines qualitatively in Fig. \[fig:qualitative\]. The spectrograms show a sound of water filling a container in five seconds with a 0.4 second mask at three second. The sound of Griffin-Lim GT has no mask and thus depicts how the spectrogram would look like after undergoing Griffin-Lim algorithm.

In baselines, we discover that SimilarityGraph and DeepPrior perform well on inpainting the water sound. The environmental sounds in ESC-50 contain a lot of repeating structures. Due to this reason, the result of SimilarityGraph intuitively sounds great by with its copy and paste solution. Note that SimilarityGraph fails on most SC09 cases, as there are no repetitive structures that could be pasted in cases of zero to nine. With results from DeepPrior, which is good at extrapolating local correlation, we show that sounds, like images, have local property as well.

We found that DeepPrior does surprisingly well on audio inpainting, extrapolating implicit structures embedded in spectrograms. WaveGlow inpaints with sheer noise and GMCNN fails and inpaints sheer silence.

Our proposed method inpaints meaningful elements instead of sheer noise or pure silence in the masked part, as shown in Fig. \[fig:qualitative\]. Compared 