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Review 4: "The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection"

This preprint reports on a machine learning model for detecting COVID-19 by analyzing patients’ cough sounds. Reviewers deemed the findings potentially informative and promising, with a few limitations that could be addressed.

Published onApr 13, 2022
Review 4: "The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection"
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key-enterThis Pub is a Review of
The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection

AbstractPurposeThe 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.MethodsWith 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.ResultsThe 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 positive/negative: 210/1,201).ConclusionsBased on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection model, we find that the machine learning method to a certain extent relies on acoustic features showing higher effects in conventional group difference testing.

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.


This is a good, constructive paper explaining the feature selection and machine learning algorithms to distinguish between COVID and non-COVID coughs. I have some revisions to improve this paper:

  1. In the abstract, it says: "Our automatic COVID-19 detection model performs significantly above chance level". Can you please compare your results with the results mentioned in some other existing papers?

  2. It is good to see the performance of both the linear and non-linear classifiers. But, what about DNN classifiers, such as CNNs? Many papers which have been cited use deep architectures and the results are significantly better.

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