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.

发展了一个基于自监督学习的深度学习系统，通过线性探测在 33 个健康声学任务上建立了一个先进的健康音频嵌入模型。

HeAR - 健康声学表示

HeAR -- Health Acoustic Representations

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。