Classification of COVID-19 from Cough Using Autoregressive Predictive Coding Pretraining and Spectral Data Augmentation
(Oral presentation)
| John Harvill (University of Illinois at Urbana-Champaign, USA), Yash R. Wani (University of Chicago, USA), Mark Hasegawa-Johnson (University of Illinois at Urbana-Champaign, USA), Narendra Ahuja (University of Illinois at Urbana-Champaign, USA), David Beiser (University of Chicago, USA), David Chestek (University of Illinois at Chicago, USA) |
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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 fine-tuning 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.
