论文标题
Sound-DR：可靠的声音数据集和基线人工智能系统，用于呼吸系统疾病
Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses
论文作者
论文摘要
随着呼吸道疾病的负担继续承担全球的社会，本文提出了人类声音的高质量和可靠的数据集，用于研究呼吸系统疾病，包括肺炎和Covid-19。它包括咳嗽，呼吸和鼻子呼吸，以及有关临床特征的元数据。我们还开发了一种概念验证系统，用于建立基准和对多个数据集（例如Coswara和Coughvid）进行基准测试。我们的全面实验表明，Sound-DR数据集具有更丰富的功能，更好的性能，并且对各种机器学习任务中的数据集偏移更为强大。对于移动设备上的各种实时应用程序，这是有希望的。拟议的数据集和系统将作为支持医疗保健专业人员诊断呼吸系统疾病的实用工具。数据集和代码可在此处公开可用：https：//github.com/reml-ai/sound-dr/。
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/.
