  Improving generalization is a major challenge in audio classification due to
labeled data scarcity. Self-supervised learning (SSL) methods tackle this by
leveraging unlabeled data to learn useful features for downstream
classification tasks. In this work, we propose an augmented contrastive SSL
framework to learn invariant representations from unlabeled data. Our method
applies various perturbations to the unlabeled input data and utilizes
contrastive learning to learn representations robust to such perturbations.
Experimental results on the Audioset and DESED datasets show that our framework
significantly outperforms state-of-the-art SSL and supervised learning methods
on sound/event classification tasks.
