  Self-supervised representation learning can mitigate the limitations in
recognition tasks with few manually labeled data but abundant unlabeled
data---a common scenario in sound event research. In this work, we explore
unsupervised contrastive learning as a way to learn sound event
representations. To this end, we propose to use the pretext task of contrasting
differently augmented views of sound events. The views are computed primarily
via mixing of training examples with unrelated backgrounds, followed by other
data augmentations. We analyze the main components of our method via ablation
experiments. We evaluate the learned representations using linear evaluation,
and in two in-domain downstream sound event classification tasks, namely, using
limited manually labeled data, and using noisy labeled data. Our results
suggest that unsupervised contrastive pre-training can mitigate the impact of
data scarcity and increase robustness against noisy labels, outperforming
supervised baselines.
