  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
