Imperial College London
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ProfessorBjoernSchuller

Faculty of Engineering, Department of Computing

Professor of Artificial Intelligence
 
 
 
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Contact

 

+44 (0)20 7594 8357bjoern.schuller Website

 
 
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Location

 

574Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ren:2022:10.1101/2022.03.01.22271693,
author = {Ren, Z and Chang, Y and Bartl-Pokorny, KD and Pokorny, FB and Schuller, BW},
doi = {10.1101/2022.03.01.22271693},
title = {The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection},
url = {http://dx.doi.org/10.1101/2022.03.01.22271693},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:sec><jats:title>Purpose</jats:title><jats:p>The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19’s transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge on the acoustic characteristics of COVID-19 cough sounds is limited, but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>With the theory of computational paralinguistics, we analyse the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i. e., a standardised set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The experimental results demonstrate that a set of acoustic parameters of cough sounds, e. g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, are relevant for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our automatic COVID-19 detection model performs significantly above chance level, i. e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positiv
AU  - Ren,Z
AU  - Chang,Y
AU  - Bartl-Pokorny,KD
AU  - Pokorny,FB
AU  - Schuller,BW
DO  - 10.1101/2022.03.01.22271693
PY  - 2022///
TI  - The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection
UR  - http://dx.doi.org/10.1101/2022.03.01.22271693
ER  -
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