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.

通过使用自我监督学习框架 SimCLR 和 Slowfast NFNet，我们对健康声学进行了对比学习，在各种健康应用中展现了更好的性能，同时深入分析了音频增强策略的有效性。发现组合使用增强策略可以产生超出每个策略单独应用时的协同效应。