  In this article, we adapted five recent SSL methods to the task of audio
classification. The first two methods, namely Deep Co-Training (DCT) and Mean
Teacher (MT), involve two collaborative neural networks. The three other
algorithms, called MixMatch (MM), ReMixMatch (RMM), and FixMatch (FM), are
single-model methods that rely primarily on data augmentation strategies. Using
the Wide-ResNet-28-2 architecture in all our experiments, 10% of labeled data
and the remaining 90% as unlabeled data for training, we first compare the
error rates of the five methods on three standard benchmark audio datasets:
Environmental Sound Classification (ESC-10), UrbanSound8K (UBS8K), and Google
Speech Commands (GSC). In all but one cases, MM, RMM, and FM outperformed MT
and DCT significantly, MM and RMM being the best methods in most experiments.
On UBS8K and GSC, MM achieved 18.02% and 3.25% error rate (ER), respectively,
outperforming models trained with 100% of the available labeled data, which
reached 23.29% and 4.94%, respectively. RMM achieved the best results on ESC-10
(12.00% ER), followed by FM which reached 13.33%. Second, we explored adding
the mixup augmentation, used in MM and RMM, to DCT, MT, and FM. In almost all
cases, mixup brought consistent gains. For instance, on GSC, FM reached 4.44%
and 3.31% ER without and with mixup. Our PyTorch code will be made available
upon paper acceptance at https:// github. com/ Labbe ti/ SSLH.
