mlsanityData to 7 Oct 2026

Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds

arXiv 2009.08790 ↗First posted 17 Sep 2020cs.SD cs.LG eess.AScited by 24

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Abstract (arXiv)

Testing capacity for COVID-19 remains a challenge globally due to the lack of adequate supplies, trained personnel, and sample-processing equipment. These problems are even more acute in rural and underdeveloped regions. We demonstrate that solicited-cough sounds collected over a phone, when analysed by our AI model, have statistically significant signal indicative of COVID-19 status (AUC 0.72, t-test,p <0.01,95% CI 0.61-0.83). This holds true for asymptomatic patients as well. Towards this, we collect the largest known(to date) dataset of microbiologically confirmed COVID-19 cough sounds from 3,621 individuals. When used in a triaging step within an overall testing protocol, by enabling risk-stratification of individuals before confirmatory tests, our tool can increase the testing capacity of a healthcare system by 43% at disease prevalence of 5%, without additional supplies, trained personnel, or physical infrastructure

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 9; 2023: 2; 2024: 0; 2025: 2; 2026: 1.

24 papers.

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  1. 2022Vocalsound: A Dataset for Improving Human Vocal Sounds Recognitioncited by 62
  2. 2021Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classificationcited by 16
  3. 2021A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levelscited by 12
  4. 2022Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkerscited by 12
  5. 2021Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19cited by 7
  6. 2021Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Coughcited by 7
  7. 2021Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Datacited by 6
  8. 2021Evaluating the COVID-19 Identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from Audio Challengescited by 5
  9. 2023Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Surveycited by 5
  10. 2022COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformerscited by 4
  11. 2025Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluationcited by 4
  12. 2022Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Coughcited by 3
12 more
  1. 2022TB or not TB? Acoustic cough analysis for tuberculosis classificationcited by 3
  2. 2021End-2-End COVID-19 Detection from Breath & Cough Audiocited by 2
  3. 2021Deep Neural Network Based Respiratory Pathology Classification Using Cough Soundscited by 2
  4. 2022TinyM2Net: A Flexible System Algorithm Co-designed Multimodal Learning Framework for Tiny Devicescited by 2
  5. 2022A Summary of the ComParE COVID-19 Challengescited by 2
  6. 2022COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infectioncited by 1
  7. 2025Detection of Disease on Nasal Breath Sound by New Lightweight Architecture: Using COVID-19 as An Examplecited by 1
  8. 2021Impact of data-splits on generalization: Identifying COVID-19 from cough and contextcited by 0
  9. 2021An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classificationcited by 0
  10. 2022On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voicecited by 0
  11. 2023EDAC: Efficient Deployment of Audio Classification Models For COVID-19 Detectioncited by 0
  12. 2026Benchmarking AI for low-resource contexts: Thinking beyond leaderboardscited by 0