  Self-supervised learning (SSL) learns knowledge from a large amount of
unlabeled data, and then transfers the knowledge to a specific problem with a
limited number of labeled data. SSL has achieved promising results in various
domains. This work addresses the problem of segment-level general audio SSL,
and proposes a new transformer-based teacher-student SSL model, named ATST. A
transformer encoder is developed on a recently emerged teacher-student baseline
scheme, which largely improves the modeling capability of pre-training. In
addition, a new strategy for positive pair creation is designed to fully
leverage the capability of transformer. Extensive experiments have been
conducted, and the proposed model achieves the new state-of-the-art results on
almost all of the downstream tasks.
