  Health-related acoustic signals, such as cough and breathing sounds, are
relevant for medical diagnosis and continuous health monitoring. Most existing
machine learning approaches for health acoustics are trained and evaluated on
specific tasks, limiting their generalizability across various healthcare
applications. In this paper, we leverage a self-supervised learning framework,
SimCLR with a Slowfast NFNet backbone, for contrastive learning of health
acoustics. A crucial aspect of optimizing Slowfast NFNet for this application
lies in identifying effective audio augmentations. We conduct an in-depth
analysis of various audio augmentation strategies and demonstrate that an
appropriate augmentation strategy enhances the performance of the Slowfast
NFNet audio encoder across a diverse set of health acoustic tasks. Our findings
reveal that when augmentations are combined, they can produce synergistic
effects that exceed the benefits seen when each is applied individually.
