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

Abstract

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/.

9 pages, PHMAP2023, PHM

References in corpus (7)

• Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
• Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis
• The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms
• Towards Learning a Universal Non-Semantic Representation of Speech
• XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning
• FRILL: A Non-Semantic Speech Embedding for Mobile Devices
• A Cough-based deep learning framework for detecting COVID-19