  Fast and affordable solutions for COVID-19 testing are necessary to contain
the spread of the global pandemic and help relieve the burden on medical
facilities. Currently, limited testing locations and expensive equipment pose
difficulties for individuals trying to be tested, especially in low-resource
settings. Researchers have successfully presented models for detecting COVID-19
infection status using audio samples recorded in clinical settings [5, 15],
suggesting that audio-based Artificial Intelligence models can be used to
identify COVID-19. Such models have the potential to be deployed on smartphones
for fast, widespread, and low-resource testing. However, while previous studies
have trained models on cleaned audio samples collected mainly from clinical
settings, audio samples collected from average smartphones may yield suboptimal
quality data that is different from the clean data that models were trained on.
This discrepancy may add a bias that affects COVID-19 status predictions. To
tackle this issue, we propose a multi-branch deep learning network that is
trained and tested on crowdsourced data where most of the data has not been
manually processed and cleaned. Furthermore, the model achieves state-of-art
results for the COUGHVID dataset [16]. After breaking down results for each
category, we have shown an AUC of 0.99 for audio samples with COVID-19 positive
labels.
