In addition to mass testing, radar remote life sensing technology offers non-contact applications to combat COVID-19 including heart rate tracking, identity authentication, indoor monitoring and gesture recognition ( 6). Sensitivities of 58 % have been reported for self-administered LFTs ( 4), unacceptably low when used to detect active virus, a context where high sensitivity is essential to prevent the reintegration into society of falsely reassured infected test recipients ( 5). However, physical mass testing methods, such as the Lateral Flow Test (LFT) have come under criticism since the tests divert limited resources from more critical services ( 2, 3) and due to suboptimal diagnostic accuracy. Mass testing schemes offer the option to monitor and implement a selective isolation policy to control the pandemic without the need for regional or national lockdown ( 1). The current coronavirus pandemic (COVID-19), caused by the severe-acute-respiratory-syndrome-coronavirus 2 (SARS-CoV-2), has infected a confirmed 126 million people and resulted in 2,776,175 deaths (WHO) 1. We also present the results of the cross dataset experiments with CIdeR that show the limitations of using the current COVID-19 datasets jointly to build a collective COVID-19 classifier. CIdeR achieves significant improvements over several baselines. In the current study, we demonstrate the potential of CIdeR at binary COVID-19 diagnosis from both the COVID-19 Cough and Speech Sub-Challenges of INTERSPEECH 2021, ComParE and DiCOVA.
CIdeR is an end-to-end deep learning neural network originally designed to classify whether an individual is COVID-19-positive or COVID-19-negative based on coughing and breathing audio recordings from a published crowdsourced dataset.
We report on cross-running the modified version of recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCOVA. Although these classifiers have shown strong performances on the datasets on which they are trained, their methodological adaptation to new datasets with different modalities has not been explored.
Several machine learning-based COVID-19 classifiers exploiting vocal biomarkers of COVID-19 has been proposed recently as digital mass testing methods. 3Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.2Department of Radiology, North Bristol NHS Trust, Bristol, United Kingdom.1GLAM–Group on Language, Audio, and Music, Imperial College London, London, United Kingdom.Alican Akman 1 * †, Harry Coppock 1 †, Alexander Gaskell 1, Panagiotis Tzirakis 1, Lyn Jones 2 and Björn W.
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