This report describes our submission to BHI 2023 Data Competition: Sensor challenge. Our Audio Alchemists team designed an acoustic-based COVID-19 diagnosis system, Cough to COVID-19 (C2C), and won the 1st place in the challenge. C2C involves three key contributions: pre-processing of input signals, cough-related representation extraction leveraging Wav2vec2.0, and data augmentation. Through experimental findings, we demonstrate C2C's promising potential to enhance the diagnostic accuracy of COVID-19 via cough signals. Our proposed model achieves a ROC-AUC value of 0.7810 in the context of COVID-19 diagnosis. The implementation details and the python code can be found in the following link: https://github.com/Woo-jin-Chung/BHI_2023_challenge_Audio_Alchemists

翻译：本报告描述了我们在BHI 2023数据竞赛：传感器挑战赛中的参赛方案。我们的Audio Alchemists团队设计了一套基于声学的COVID-19诊断系统——Cough to COVID-19 (C2C)，并在该挑战赛中荣获第一名。C2C包含三项关键贡献：输入信号的预处理、利用Wav2vec2.0提取咳嗽相关表征，以及数据增强。通过实验结果，我们证明了C2C在利用咳嗽信号提升COVID-19诊断准确性方面具有巨大潜力。我们提出的模型在COVID-19诊断中实现了0.7810的ROC-AUC值。实现细节及Python代码可在以下链接中找到：https://github.com/Woo-jin-Chung/BHI_2023_challenge_Audio_Alchemists