single spoken word from zero to nine by different speakers in uncontrolled environments. Since its first proposal by [@donahue2018adversarial], it has been used in many audio generation research [@donahue2018adversarial; @marafioti2019adversarial] and often regarded as the most common baseline in the area. (just as MNIST dataset [@lecun1998mnist] in written digit recognition, although examples in SC09 are more complicated ($\mathbb{R}^{16000}$) than MNIST ($\mathbb{R}^{28*28=784}$))

#### **ESC-50.**

ESC-50 dataset [@piczak2015dataset] is a labeled dataset for environmental sound classification, including 2000 5-second long environmental audio recordings of 50 semantic classes (40 examples per class) from 5 categories: animals, natural soundscapes & water sounds, human non-speech sounds, interior/domestic sounds and exterior/urban noises. Compared to SC09, examples in ESC-50 are more repetitive and thus easier for patch- and example-based methods but harder for learning-based methods for it has more classes and fewer data per class.

Benchmark Procedure
-------------------

We setup the long audio inpainting benchmark to compare baselines, including WaveGlow [@prenger2019waveglow], SimilarityGraph [@perraudin2018inpainting], DeepPrior [@ulyanov2018deep] and GMCNN [@wang2018image]. For SC09, we train all the models on the whole training set with random masking of 0.2 second and without any data augmentation. We perform evaluation on the testing set with fixed mask from 0.4 $\sim$ 0.6 second. For 