enlarging the receptive field has little benefit or even negative effect for training. That indicates, after some threshold, our model is complicated enough and is able to restore the mask.

#### **Mask ratio.**

We train several models by altering the mask length from 0.1 to 0.25 second and keep the receptive field fixed. We found that our model could adapt to different mask lengths (from 0.1 to 0.16 seconds), with a fixed receptive field, as long as the mask size is smaller or equals to receptive field. This, on the other side, again confirms that the receptive field has to be at least similar to the mask size to perform successful inpainting. Also by observing the failure cases, the inpainted sound at the mask position trailed off at first, vanished at the middle, and then appeared in the end. It indicates that if the mask field is too big, the receptive field will not be sufficient to gather enough information to rebuild the whole masked part.

Discussion and Future Work
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#### **Receptive field and model architecture.**

To the best of our knowledge, we are the first to evaluate different baselines and model architectures for deep long audio inpainting We 