  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
https://github.com/evelyn0414/OPERA.
