Classification of COVID-19 from cough using autoregressive predictive coding pretraining and spectral data augmentation

Serum and saliva-based testing methods have been crucial to slowing the COVID-19 pandemic, yet have been limited by slow throughput and cost. A system able to determine COVID- 19 status from cough sounds alone would provide a low cost, rapid, and remote alternative to current testing methods. We explore the applicability of recent techniques such as pre-training and spectral augmentation in improving the performance of a neural cough classification system. We use Autoregressive Predictive Coding (APC) to pre-train a unidirectional LSTM on the COUGHVID dataset. We then generate our final model by finetuning added BLSTM layers on the DiCOVA challenge dataset. We perform various ablation studies to see how each component impacts performance and improves generalization with a small dataset. Our final system achieves an AUC of 85.35 and places third out of 29 entries in the DiCOVA challenge.