Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals

TL;DR

This paper explores how to optimize audio augmentations within a self-supervised contrastive learning framework to improve the generalizability of health-related acoustic signal analysis across various medical tasks.

Contribution

It introduces an in-depth analysis of augmentation strategies for contrastive learning of health acoustics using a Slowfast NFNet backbone, highlighting the importance of augmentation combinations.

Findings

Augmentation strategies significantly improve model performance.

Combined augmentations have synergistic effects.

Optimized augmentations enhance generalizability across tasks.

Abstract

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.