to provide a solution.

We harness the demo website[^5] to perform inpainting. Since their framework requires that the mask to be at least 3 seconds away from the start and the end of the audio, we perform duplication before uploading audio to the website. For SC09, we duplicate both the front and rear 0.4 second for 8 times, generating an audio that is 0.4 \* 16 + 0.2 = 6.6 second long (Hence, the mask is from 3.2 to 3.4 second). And for ESC-50, we duplicate only the rear 1.6 second twice, generating an audio that is 3 + 0.4 + 1.6 \* 2 = 6.6 second long (Hence, the mask is from 3 t o 3.4 second). After inpainting, we extract the segment that corresponds to the original audio. Note that since the algorithm replaces the masked part with a audio segment from the same signal, the position of the original audio might shift slightly.

Evaluation Metrics
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To evaluate different methods numerically, we calculate the masked $l_1$ error (Eq. \[l1\_loss\]) and perceptual distance (Eq. \[percputal\_loss\]) between the outputs and ground truths on waveforms and sepctrograms, as explained in Sec. \[sec:loss\_functions\]. For fair comparison of perceptual distance on waveforms, 