The Acoustic Dissection of Cough

Diving Into Machine Listening-based COVID-19 Analysis and Detection

verfasst von: Zhao Ren, Yi Chang, Katrin D. Bartl-Pokorny, Florian B. Pokorny, Björn W. Schuller
Abstract: Objectives: 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 of 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. Methods: By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i.e., a standardized 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. Results: 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, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general 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 positive/negative: 210/1,201). Conclusions: Based on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models.
Organisationseinheit(en): Forschungszentrum L3S
Externe Organisation(en): Universität Augsburg
Imperial College London
Medical University of Graz
Typ: Artikel
Journal: Journal of voice
ISSN: 0892-1997
Publikationsdatum: 15.06.2022
Publikationsstatus: Elektronisch veröffentlicht (E-Pub)
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Hals-Nasen-Ohrenheilkunde, Sprechen und Hören, Ausgebildete Pflegekräfte (LPN und LVN)
Ziele für nachhaltige Entwicklung: SDG 3 – Gute Gesundheit und Wohlergehen
Elektronische Version(en): https://doi.org/10.1016/j.jvoice.2022.06.011 (Zugang: Offen)

BibTeX

@article{69f5d11040fd48a0b6e553f71fc7b489,
title = "The Acoustic Dissection of Cough: Diving Into Machine Listening-based COVID-19 Analysis and Detection",
abstract = "Objectives: 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 of 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. Methods: By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i.e., a standardized 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. Results: 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, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general 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 positive/negative: 210/1,201). Conclusions: Based on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in conventional group difference testing are also higher weighted in the machine learning models.",
keywords = "Acoustics, Automatic disease detection, Computational paralinguistics, Cough, COVID-19",
author = "Zhao Ren and Yi Chang and Bartl-Pokorny, {Katrin D.} and Pokorny, {Florian B.} and Schuller, {Bj{\"o}rn W.}",
note = "Funding Information: The authors want to express their gratitude to the holders of the COUGHVID crowdsourcing dataset for providing collected data for research purposes. Above all, thanks to all participants for their coughs. This work is supported by the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie Actions Initial Training Network European Training Network project TAPAS (grant number 766287 ), the Deutsche Forschungsgemeinschaft's Reinhart Koselleck project AUDI0NOMOUS (grant number 442218748 ), and the Federal Ministry of Education and Research (BMBF), Germany , under the project LeibnizKILabor (grant No. 01DD20003 ). ",
year = "2022",
month = jun,
day = "15",
doi = "10.1016/j.jvoice.2022.06.011",
language = "English",
journal = "Journal of voice",
issn = "0892-1997",
publisher = "Mosby Inc.",
}