The Acoustic Dissection of Cough

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

authored by

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.

Organisation(s)

L3S Research Centre

External Organisation(s)

University of Augsburg
Imperial College London
Medical University of Graz

Type

Article

Journal

Journal of voice

ISSN

0892-1997

Publication date

15.06.2022

Publication status

E-pub ahead of print

Peer reviewed

Yes

ASJC Scopus subject areas

Otorhinolaryngology, Speech and Hearing, LPN and LVN

Sustainable Development Goals

SDG 3 - Good Health and Well-being

Electronic version(s)

https://doi.org/10.1016/j.jvoice.2022.06.011 (Access: Open)
https://doi.org/10.15488/15816 (Access: Open)