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

no code implementations • 30 Jul 2021 • Alican Akman, Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Lyn Jones, Björn W. Schuller

We report on cross-running the recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCOVA.

COVID-19 Diagnosis

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

1 code implementation • 7 Jan 2021 • Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Alice Baird, Lyn Jones, Björn W. Schuller

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.