  As the burden of respiratory diseases continues to fall on society worldwide,
this paper proposes a high-quality and reliable dataset of human sounds for
studying respiratory illnesses, including pneumonia and COVID-19. It consists
of coughing, mouth breathing, and nose breathing sounds together with metadata
on related clinical characteristics. We also develop a proof-of-concept system
for establishing baselines and benchmarking against multiple datasets, such as
Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr
dataset has richer features, better performance, and is more robust to dataset
shifts in various machine learning tasks. It is promising for a wide range of
real-time applications on mobile devices. The proposed dataset and system will
serve as practical tools to support healthcare professionals in diagnosing
respiratory disorders. The dataset and code are publicly available here:
https://github.com/ReML-AI/Sound-Dr/.
