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

John Harvill, Yash R. Wani, Mark Hasegawa-Johnson, Narendra Ahuja, David Beiser, David Chestek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
PublisherInternational Speech Communication Association
Pages4261-4265
Number of pages5
ISBN (Electronic)9781713836902
DOIs
StatePublished - 2021
Event22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 - Brno, Czech Republic
Duration: Aug 30 2021Sep 3 2021

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume6
ISSN (Print)2308-457X
ISSN (Electronic)1990-9772

Conference

Conference22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
Country/TerritoryCzech Republic
CityBrno
Period8/30/219/3/21

Keywords

  • COVID-19 classification
  • Cough
  • Data augmentation
  • DiCOVA
  • Pretraining

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation

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