& & 134.3M & 96.0%\
&& ResNet50 & & 23.5M & 96.3%\
&& ResNet50 & $\times$ & 23.5M & 94.7%\
ESC-50 & Wave. & SoundNet & & 14.7M & 66.3%\
&& SoundNet & $\times$ & 14.7M & 61.0%\
&Spec. & VGG16 & & 134.4M & 83.5%\
&& ResNet50 & & 23.6M & 82.0%\
&& ResNet50 & $\times$ & 23.6M & 77.0%\

![Spectrograms of audio inpainting results. SimilarityGraph performs well as sounds in ESC-50 tend to embed repetitive structures for example-based method to exploit. Also, we show that despite its long processing time, DeepPrior also excels at spectrogram inpainting aside from image inpainting. []{data-label="fig:qualitative"}](audio_inpainting_qualitative_crop.png){width="0.9\linewidth"}

Quantitative Results
--------------------

#### **Sound classification.**

For perceptual losses evaluation, we finetune SoundNet or train ResNet from scratch on audio classification with cross entropy for waveform and spectrogram respectively. The classification accuracy of each model is reported in Table \[tab:perceptual\_loss\_classification\]. We could observe that for ESC-50, spectrogram based classification models outperform those based on waveform, while in SC09, this does not hold. This might be caused by the input dimension. That is, raw waveforms in ESC-50 have more samples (22050 $\times$ 5 = 110250 samples) than those in SC09, making it harder for models based on 1-d 