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arXiv · 2311.00364

C2C: Cough to COVID-19 Detection in BHI 2023 Data Challenge

Woo-Jin Chung·Miseul Kim·Hong-Goo Kang
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Abstract

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

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Woo-Jin Chung, Miseul Kim, Hong-Goo Kang. 2023-11-01. C2C: Cough to COVID-19 Detection in BHI 2023 Data Challenge. https://arxiv.org/abs/2311.00364

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