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

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses