Respiratory audio, such as coughing and breathing sounds, has predictive
power for a wide range of healthcare applications, yet is currently
under-explored. The main problem for those applications arises from the
difficulty in collecting large labeled task-specific data for model
development. Generalizable respiratory acoustic foundation models pretrained
with unlabeled data would offer appealing advantages and possibly unlock this
impasse. However, given the safety-critical nature of healthcare applications,
it is pivotal to also ensure openness and replicability for any proposed
foundation model solution. To this end, we introduce OPERA, an OPEn Respiratory
Acoustic foundation model pretraining and benchmarking system, as the first
approach answering this need. We curate large-scale respiratory audio datasets
(~136K samples, 440 hours), pretrain three pioneering foundation models, and
build a benchmark consisting of 19 downstream respiratory health tasks for
evaluation. Our pretrained models demonstrate superior performance (against
existing acoustic models pretrained with general audio on 16 out of 19 tasks)
and generalizability (to unseen datasets and new respiratory audio modalities).
This highlights the great promise of respiratory acoustic foundation models and
encourages more studies using OPERA as an open resource to accelerate research
on respiratory audio for health. The system is accessible from
this https URL

我们介绍了 OPERA，这是第一个满足医疗应用需求的开放呼吸声基础模型预训练和基准系统，我们筛选了大规模的呼吸音频数据集，预训练了三个创新的基础模型，并构建了一个包含 19 个下游呼吸健康任务的基准，我们的预训练模型在 16 个任务中表现出优越性能，并且具有推广性，对于未见数据集和新的呼吸音频模态具有良好的泛化能力，这突显了呼吸声基础模型的巨大潜力，并鼓励更多使用 OPERA 进行呼吸音频研究加速的研究。