the ESC-50 dataset, we train models with the first 1600 sound clips (first to fourth fold) with random masks of 0.4 second. Sound clips are copied twice to 10 seconds and then randomly cropped to 5 seconds during training. The first 200 sound clips of the fifth fold is used for validation while testing is done on the other 200 sound clips with fixed mask from 3.0 $\sim$ 3.4 second. For models that require longer inputs, we apply zero padding. Note that after inpainting, we paste the unmasked segments from input to the output.

Baseline Implementation Details
-------------------------------

### WaveGlow

is a flow-based vocoder that transforms a melspectrogram to its corresponding final waveform. It combines essence of WaveNet and Glow and directly learns the data distribution. We modify it to take a masked melspectrogram instead of a complete one as input, using the codes provided by NVidia[^2]. We train each model for 100000 epochs with batch size 2 and 4 for SC09 and ESC-50 respectively.

### Deep Image Prior

performs well on several image restoration tasks including inpainting by simply using the structure of a neural network and the corrupted image without any training beforehand. We harness the inpainting script 