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arXiv · 2102.08359

End-2-End COVID-19 Detection from Breath & Cough Audio

Harry Coppock·Alexander Gaskell·Panagiotis Tzirakis·Alice Baird·Lyn Jones·Björn W. Schuller
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Abstract

Our main contributions are as follows: (I) We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples, achieving ROC-AUC of 0.846; (II) Our model, the COVID-19 Identification ResNet, (CIdeR), has potential for rapid scalability, minimal cost and improving performance as more data becomes available. This could enable regular COVID-19 testing at apopulation scale; (III) We introduce a novel modelling strategy using a custom deep neural network to diagnose COVID-19 from a joint breath and cough representation; (IV) We release our four stratified folds for cross parameter optimisation and validation on a standard public corpus and details on the models for reproducibility and future reference.

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Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Alice Baird, Lyn Jones, Björn W. Schuller. 2021-01-07. End-2-End COVID-19 Detection from Breath & Cough Audio. https://arxiv.org/abs/2102.08359

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