convolutions with limited receptive field size to extract high-level features. On the contrary, after STFT, spectrograms are only in 1024 $\times$ 400 and 1024 $\times$ 80 for ESC-50 and SC09 respectively, which are reasonable sizes for image classification models and the size difference is smaller for the two datasets.

Another interesting point is that the pre-trained weights on ImageNet boost spectrogram classification for about 5%, even though the applications and domains are quite different (3-channels natural images vs 1-channel spectrograms). It possibly imply that pre-training on other datasets such as Google Audio set [@gemmeke2017audio] could further improve the perceptual losses and metrics.

[|c|c|c|c|c|c|c|c|c|c|]{} & & &\
Method & ML1 $\downarrow$ & SSIM $\uparrow$&

  ----------------------
          Wave.
   P. Dist.$\downarrow$
  ----------------------

&

  -----------------------
           Spec.
   P. Dist. $\downarrow$
  -----------------------

& ML1 $\downarrow$ & SSIM $\uparrow$&

  ----------------------
          Wave.
   P. Dist.$\downarrow$
  ----------------------

&

  -----------------------
           Spec.
   P. Dist. $\downarrow$
  -----------------------

&

  ----------------------
          Infer.
   Speed^\*^ $\uparrow$
  ----------------------

\
Masked Input & 0.011125 & 0.675040 & 0.006231 & 0.079655 & 0.067510 & 0.648608 & 0.007740 & 0.542866 & –\
Griffin-Lim GT & 0.021293 & 0.808092 & 0.004214 & 0.021372 & 0.004539 & 0.978142 & 0.000784 & 0.007324 & –\
WaveRNN & $\times$ & $\times$ & $\times$ & $\times$ & 