Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals
This work addresses the need for more generalizable models in healthcare applications using acoustic signals, but it is incremental as it focuses on optimizing existing methods rather than introducing new paradigms.
The paper tackled the problem of limited generalizability in machine learning for health-related acoustic signals by optimizing audio augmentations within a SimCLR framework with a Slowfast NFNet backbone, resulting in enhanced performance across diverse health acoustic tasks through synergistic effects from combined augmentations.
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.