ArticlePattern recognition2022
AI-Based human audio processing for COVID-19: A comprehensive overview.
Article in Pattern recognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- Longitudinal voice monitoring in a decentralized Bring Your Own Device trial for respiratory illness detection.NPJ digital medicine · 2025Article
- Multimodal Technologies for Remote Assessment of Neurological and Mental Health.Journal of speech, language, and hearing research : JSLHR · 2024Review
- Cough2COVID-19 detection using an enhanced multi layer ensemble deep learning framework and CoughFeatureRanker.Scientific reports · 2024Article
- Acoustic and Clinical Data Analysis of Vocal Recordings: Pandemic Insights and Lessons.Diagnostics (Basel, Switzerland) · 2024Article
- Voice EHR: introducing multimodal audio data for health.Frontiers in digital health · 2024Article
- COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests.Scientific reports · 2023Article
- COVID-19 and Rumors: A Dynamic Nested Optimal Control Model.Pattern recognition · 2023Article
- Transfer learning for the efficient detection of COVID-19 from smartphone audio data.Pervasive and mobile computing · 2023Article
- HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.Frontiers in digital health · 2023Review
- The Voice of the Body: Why AI Should Listen to It and an Archive.Cyborg and bionic systems (Washington, D.C.) · 2023Review
- Editorial: Human-centred computer audition: sound, music, and healthcare.Frontiers in digital health · 2023Article
- Exploring machine learning for audio-based respiratory condition screening: A concise review of databases, methods, and open issues.Experimental biology and medicine (Maywood, N.J.) · 2022Review
- Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation.Journal of medical Internet research · 2022Article
- A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19.Annals of medicine and surgery (2012) · 2022Review
- CR19: a framework for preliminary detection of COVID-19 in cough audio signals using machine learning algorithms for automated medical diagnosis applications.Journal of ambient intelligence and humanized computing · 2022Article
- A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.Frontiers in artificial intelligence · 2022Review
- Speech as a Biomarker for COVID-19 Detection Using Machine Learning.Computational intelligence and neuroscience · 2022Article
- Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review.Soft computing · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Coronavirus (COVID-19) pandemic impelled several research efforts, from collecting COVID-19 patients' data to screening them for virus detection. Some COVID-19 symptoms are related to the functioning of the respiratory system that influences speech production; this suggests research on identifying markers of COVID-19 in speech and other human generated audio signals. In this article, we give an overview of research on human audio signals using 'Artificial Intelligence' techniques to screen, diagnose, monitor, and spread the awareness about COVID-19. This overview will be useful for developing automated systems that can help in the context of COVID-19, using non-obtrusive and easy to use bio-signals conveyed in human non-speech and speech audio productions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.