ReviewFrontiers in digital health2021
COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis.
Review in Frontiers in digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses 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
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.Current topics in medicinal chemistry · 2024Pooled it
- The Use of Audio Signals for Detecting COVID-19: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Healthcare Expenditure and COVID-19 in Europe: Correlation, Entropy, and Functional Data Analysis-Based Prediction of Hospitalizations and ICU Admissions.Entropy (Basel, Switzerland) · 2025Article
- Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics.Sensors (Basel, Switzerland) · 2023Article
- The COVID-19 Pandemic: How Technology Is Reshaping Public Health and Medicine.Bioengineering (Basel, Switzerland) · 2023Article
- Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.Expert systems with applications · 2023Article
- Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model.Expert systems with applications · 2023Article
- Transfer learning for the efficient detection of COVID-19 from smartphone audio data.Pervasive and mobile computing · 2023Article
- Effects of post-acute COVID-19 syndrome on the functional brain networks of non-hospitalized individuals.Frontiers in neurology · 2023Article
- Human behavior in the time of COVID-19: Learning from big data.Frontiers in big data · 2023Review
- 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
- Augmentation of literature review of COVID-19 radiology.World journal of radiology · 2022Article
- Remote Analysis of Respiratory Sounds in Patients With COVID-19: Development of Fast Fourier Transform-Based Computer-Assisted Diagnostic Methods.JMIR formative research · 2022Article
- QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds.Diagnostics (Basel, Switzerland) · 2022Article
- A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19.Annals of medicine and surgery (2012) · 2022Review
- Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.Journal of medical systems · 2022Article
- 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
- Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network.Biomedical signal processing and control · 2022Article
- AI-Based human audio processing for COVID-19: A comprehensive overview.Pattern recognition · 2022Article
- Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study.Cognitive neurodynamics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
At the time of writing this article, the world population is suffering from more than 2 million registered COVID-19 disease epidemic-induced deaths since the outbreak of the corona virus, which is now officially known as SARS-CoV-2. However, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of COVID-19 directly or its symptoms such as breathing, dry, and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient well-being. We quickly guide further through challenges that need to be faced for real-life usage and limitations also in comparison with non-audio solutions. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.
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.