we finetune another SoundNet [@aytar2016soundnet] and VGG16 [@simonyan2014very] for perceptual distance. Please see Table \[tab:perceptual\_loss\_classification\] for detailed settings of different backbones (pre-) trained on SoundNet and ResNet50 for perceptual loss on waveforms, spectrograms respectively. In addition, we also report the structural similarity (SSIM) index [@wang2004image] on spectrograms. The inference time is reported in terms of how many sound clip could be processed per second on Intel(R) Xeon(R) Gold 6154 CPU 3.00GHz with a single V100 GPU.

[|c|c|c|c|c|c|]{}

  ---------
   Dataset
  ---------

  : Sound classification accuracy for perceptual losses/metrics on SC09 and ESC-50 testing set.[]{data-label="tab:perceptual_loss_classification"}

&

  ------
   Type
  ------

  : Sound classification accuracy for perceptual losses/metrics on SC09 and ESC-50 testing set.[]{data-label="tab:perceptual_loss_classification"}

&

  -------
   Model
  -------

  : Sound classification accuracy for perceptual losses/metrics on SC09 and ESC-50 testing set.[]{data-label="tab:perceptual_loss_classification"}

&

  --------
   Pretr.
  --------

  : Sound classification accuracy for perceptual losses/metrics on SC09 and ESC-50 testing set.[]{data-label="tab:perceptual_loss_classification"}

&

  --------
   Param.
  --------

  : Sound classification accuracy for perceptual losses/metrics on SC09 and ESC-50 testing set.[]{data-label="tab:perceptual_loss_classification"}

&

  ------
   Acc.
  ------

  : Sound classification accuracy for perceptual losses/metrics on SC09 and ESC-50 testing set.[]{data-label="tab:perceptual_loss_classification"}

\
SC09 & Wave. & SoundNet & & 14.3M & 93.4%\
&& SoundNet & $\times$ & 14.3M & 91.0%\
&Spec. & VGG16 