HeAR -- Health Acoustic Representations
This work addresses the underexplored area of health acoustics for medical machine learning, offering a scalable solution to improve generalization across multiple tasks, though it is incremental as it builds on existing self-supervised learning methods.
The authors tackled the problem of limited generalization in health acoustic deep learning systems by developing HeAR, a self-supervised model trained on 313 million audio clips, which achieved state-of-the-art performance on a benchmark of 33 tasks across 6 datasets.
Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in the medical machine learning community. The existing deep learning systems for health acoustics are often narrowly trained and evaluated on a single task, which is limited by data and may hinder generalization to other tasks. To mitigate these gaps, we develop HeAR, a scalable self-supervised learning-based deep learning system using masked autoencoders trained on a large dataset of 313 million two-second long audio clips. Through linear probes, we establish HeAR as a state-of-the-art health audio embedding model on a benchmark of 33 health acoustic tasks across 6 datasets. By introducing this work, we hope to enable and accelerate further health acoustics research.