ArticleComputers in biology and medicine2021
Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Multimodal deep learning for cardiovascular disease detection using pulse wave and vocal signals: a prediction model development and validation study.BMC medical informatics and decision making · 2026Article
- Advanced COVID-19 detection using cough signals with space reconstruction and 3D deep convolutional neural networks.Scientific reports · 2025Article
- Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm.Entropy (Basel, Switzerland) · 2025Article
- Non-invasive acoustic classification of adult asthma using an XGBoost model with vocal biomarkers.Scientific reports · 2025Article
- Vocal biomarkers correlate with FEV1 variations during methacholine challenge.Clinical and translational allergy · 2025Article
- Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm.Scientific reports · 2025Article
- SympCoughNet: symptom assisted audio-based COVID-19 detection.Frontiers in digital health · 2025Article
- COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers.IEEE journal of biomedical and health informatics · 2024Article
- Voice EHR: introducing multimodal audio data for health.Frontiers in digital health · 2024Article
- Optimized DEC: An effective cough detection framework using optimal weighted Features-aided deep Ensemble classifier for COVID-19.Biomedical signal processing and control · 2023Article
- The voice of COVID-19: Breath and cough recording classification with temporal decision trees and random forests.Artificial intelligence in medicine · 2023Article
- COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Vocal biomarker predicts fatigue in people with COVID-19: results from the prospective Predi-COVID cohort study.BMJ open · 2022Article
- Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research.Interactive journal of medical research · 2022Article
- Deep learning and machine learning-based voice analysis for the detection of COVID-19: A proposal and comparison of architectures.Knowledge-based systems · 2022Article
- Ink-lithographic fabrication of silver-nanocrystal-based multiaxial strain gauge sensors through the coffee-ring effect for voice recognition applications.Nano convergence · 2022Article
- Challenges and Opportunities of Deep Learning for Cough-Based COVID-19 Diagnosis: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- A study of using cough sounds and deep neural networks for the early detection of Covid-19.Biomedical engineering advances · 2022Article
- COVID-19 classification using thermal images.Journal of biomedical optics · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 heavily affects breathing and voice and causes symptoms that make patients' voices distinctive, creating recognizable audio signatures. Initial studies have already suggested the potential of using voice as a screening solution. In this article we present a dataset of voice, cough and breathing audio recordings collected from individuals infected by SARS-CoV-2 virus, as well as non-infected subjects via large scale crowdsourced campaign. We describe preliminary results for detection of COVID-19 from cough patterns using standard acoustic features sets, wavelet scattering features and deep audio embeddings extracted from low-level feature representations (VGGish and OpenL3). Our models achieve accuracy of 88.52%, sensitivity of 88.75% and specificity of 90.87%, confirming the applicability of audio signatures to identify COVID-19 symptoms. We furthermore provide an in-depth analysis of the most informative acoustic features and try to elucidate the mechanisms that alter the acoustic characteristics of coughs of people with COVID-19.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.