Sounds of COVID-19: exploring realistic performance of audio-based digital testing
Open accessAbstract
To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent work has shown how audio (including cough, breathing and voice) based approaches can be used for testing. However, there is a lack of exploration of how biases and methodological decisions impact these tools’ performance in practice. In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. To investigate this, we collected a large crowdsourced respiratory audio dataset through a mobile app, alongside symptoms and COVID-19 test results. Within the collected dataset, we selected 5240 samples from 2478 English-speaking participants and split them into participant-independent sets for model development and validation. In addition to controlling the language, we also balanced demographics for model training to avoid potential acoustic bias. We used these audio samples to construct an audio-based COVID-19 prediction model. The unbiased model took features extracted from breathing, coughs and voice signals as predictors and yielded an AUC-ROC of 0.71 (95% CI: 0.65–0.77). We further explored several scenarios with different types of unbalanced data distributions to demonstrate how biases and participant splits affect the performance. With these different, but less appropriate, evaluation strategies, the performance could be overestimated, reaching an AUC up to 0.90 (95% CI: 0.85–0.95) in some circumstances. We found that an unrealistic experimental setting can result in misleading, sometimes over-optimistic, performance. Instead, we reported complete and reliable results on crowd-sourced data, which would allow medical professionals and policy makers to accurately assess the value of this technology and facilitate its deployment.
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Breathing and cough sounds contain clinically relevant information and are highly related to pulmonary health.
“Breathing and cough are two types of sounds that contain clinically relevant information and highly related to pulmonary health [10,43].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [10]
A CNN-based network validated using crowdsourced voice, cough, and breathing sounds yields 0.71 ROC-AUC for COVID-19 detection, suggesting potential of crowdsourced audio for COVID-19 detection.
“A convolutional neural network (CNN) based network structure is validated using a combination of crowdsourced voice, cough and breathing sounds [10,49]. The system is developed and validated using a total of 2478 participants and yields 0.71 in terms of ROC-AUC, suggesting the potential of crowdsourced audio for COVID-19 detection.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [10]
COVID-19 has been studied using voice, breathing, and cough sounds.
“COVID-19 has been studied using different audio sounds, including voice, breathing and cough sounds [10,29,79,80].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [10]
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Classification of Respiratory Diseases and COVID-19 from Respiratory and Cough Sounds
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Self-citationDeveloping a multi-variate prediction model for COVID-19 from crowd-sourced respiratory voice data
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The sound of surgery-development of an acoustic trocar system enabling laparoscopic sound analysis
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Mobile Sensing in the COVID-19 Era: A Review
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COVID-19 Detection from Speech in Noisy Conditions
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Positive-Pair Redundancy Reduction Regularisation for Speech-Based Asthma Diagnosis Prediction
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SympCoughNet: symptom assisted audio-based COVID-19 detection
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Cough-based COVID-19 Detection with Multi-head Deep Neural Network
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Evaluation of Bag-of-Words Classifiers for COVID-19 Detection from Cough Recordings
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Artificial intelligence in COVID-19
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Deep Digital Twin Services for Personalized MPX Treatment
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CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning
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COVID-19 Detection Using Forced Cough Sounds and Medical Information
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Mitigating Sex Bias in Audio Data-Driven COPD and COVID-19 Breathing Pattern Detection Models
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- Authors
- Han, J., Xia, T., Spathis, D., Bondareva, E., Brown, C., Chauhan, J., Dang, T., Grammenos, A., Hasthanasombat, A., Floto, A., Cicuta, P., & Mascolo, C.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Xia, Tong
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2022-01-28
- Volume
- 5
- Issue
- 1
- Pages
- 16-16
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC8799654
- OpenAlex
- W3175510762
- Cited by
- 90
- References
- 39
- Citations by year
- 2026: 4 · 2025: 18 · 2024: 18 · 2023: 27 · 2022: 21 · 2021: 1
- FWCI
- 13.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Apollo
- Apollo (cc-by)
- Funders
- Cystic Fibrosis Trust
- H2020 European Research Council (833296)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Keywords
- COVID-19 pandemic, cough, lung sounds, voice analysis, demographic bias, class imbalance, overfitting, overestimation of performance, mobile app data collection
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