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

by   Harry Coppock, et al.

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.


Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challenges

We report on cross-running the recent COVID-19 Identification ResNet (CI...

A Residual Network based Deep Learning Model for Detection of COVID-19 from Cough Sounds

The present work proposes a deep-learning-based approach for the classif...

Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough

Rapid and affordable methods of testing for COVID-19 infections are esse...

UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio

We suggested a unified system with core components of data augmentation,...

Sounds of COVID-19: exploring realistic performance of audio-based digital testing

Researchers have been battling with the question of how we can identify ...

Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19

Fast and affordable solutions for COVID-19 testing are necessary to cont...

Synthesizing Cough Audio with GAN for COVID-19 Detection

For this final year project, the goal is to add to the published works w...