  Recently, semi-supervised learning (SSL) methods, in the framework of deep
learning (DL), have been shown to provide state-of-the-art results on image
datasets by exploiting unlabeled data. Most of the time tested on object
recognition tasks in images, these algorithms are rarely compared when applied
to audio tasks. In this article, we adapted four recent SSL methods to the task
of audio tagging. The first two methods, namely Deep Co-Training (DCT) and Mean
Teacher (MT) involve two collaborative neural networks. The two other
algorithms, called MixMatch (MM) 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, we first compare the four methods' accuracy on three standard
benchmark audio event datasets: Environmental Sound Classification (ESC-10),
UrbanSound8K (UBS8K), and Google Speech Commands (GSC). MM and FM outperformed
MT and DCT significantly, MM being the best method in most experiments. On
UBS8K and GSC, in particular, MM achieved 18.02% and 3.25% error rates (ER),
outperforming models trained with 100% of the available labeled data, which
reached 23.29% and 4.94% ER, respectively. Second, we explored the benefits of
using the mixup augmentation in the four algorithms. In almost all cases, mixup
brought significant gains. For instance, on GSC, FM reached 4.44% and 3.31% ER
without and with mixup.
