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

• URL: http://arxiv.org/abs/2102.08359v1
• Date: Thu, 7 Jan 2021 01:13:00 GMT
• Title: End-2-End COVID-19 Detection from Breath & Cough Audio
• Authors: Harry Coppock and Alexander Gaskell and Panagiotis Tzirakis and Alice Baird and Lyn Jones and Bj\"orn W. Schuller
• Abstract summary: We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples. We introduce a novel modelling strategy using a custom deep neural network to diagnose COVID-19 from a joint breath and cough representation.
• Score: 68.41471917650571
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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