Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Air-entraining Agents01:27

Air-entraining Agents

133
Air-entraining agents improve the durability and workability of concrete in climates with frequent freezing and thawing. These agents prevent cracks by introducing small air bubbles into the mix, creating spaces accommodating water expansion when temperatures drop. The air-entraining agents lower the surface tension of water, forming stable, small air bubbles. This method is more effective than having accidental large voids, as the intentional, smaller, and evenly distributed air voids improve...
133

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Listening to MS: AI-assisted speech analysis for diagnosis and fatigue prediction (COMMITMENT).

Frontiers in digital health·2026
Same author

Prior-aligned frequency-domain explanations for heart sound classification: a scale-consistent attribution approach.

Frontiers in artificial intelligence·2026
Same author

Discordance of Dual-Tracer PET/CT with Histopathology in a Grade I Pancreatic Neuroendocrine Tumor: A Diagnostic Conundrum.

World journal of nuclear medicine·2026
Same author

Explainable detection of machine generated music and early systematic evaluation.

Scientific reports·2026
Same author

A frequency analysis of filterbank initialisation and noise augmentation for LEAF.

Scientific reports·2026
Same author

Multi-Granularity Facial Emotional Representation With Unlabeled Data and Textual Supervision.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection.

Pattern recognition·2026
Same journal

LDM-Morph: Latent diffusion model guided deformable image registration.

Pattern recognition·2026
Same journal

Variable Priority for Unsupervised Variable Selection.

Pattern recognition·2026
Same journal

A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery.

Pattern recognition·2025
Same journal

Medical image segmentation using dual-decoder mutual teaching with a mean teacher framework.

Pattern recognition·2025
Same journal

Multi-graph Graph matching for coronary artery semantic labeling in invasive coronary angiograms.

Pattern recognition·2025
See all related articles

Related Experiment Video

Updated: Oct 21, 2025

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

2.7K

AI-Based human audio processing for COVID-19: A comprehensive overview.

Gauri Deshpande1,2, Anton Batliner1, Björn W Schuller1,3

  • 1Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.

Pattern Recognition
|September 6, 2021
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) can analyze human speech and audio signals for COVID-19 detection. This research explores AI

Keywords:
Audio processingCOVID-19Computational paralinguisticsDigital health

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

558
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.7K

Related Experiment Videos

Last Updated: Oct 21, 2025

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
06:01

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

Published on: December 9, 2022

2.7K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

558
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.7K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Speech Signal Processing

Background:

  • The COVID-19 pandemic necessitated rapid diagnostic and monitoring tools.
  • Respiratory symptoms of COVID-19 can affect speech production.
  • Human-generated audio signals offer potential non-obtrusive biomarkers.

Purpose of the Study:

  • To provide an overview of AI-driven research for COVID-19 detection using human audio signals.
  • To highlight the potential of speech and non-speech audio for COVID-19 screening, diagnosis, and monitoring.
  • To inform the development of automated audio-based COVID-19 detection systems.

Main Methods:

  • Review of existing research on Artificial Intelligence techniques applied to human audio signals.
  • Analysis of how respiratory system changes due to COVID-19 impact speech production.
  • Exploration of non-speech audio signals as potential diagnostic indicators.

Main Results:

  • AI techniques show promise in analyzing speech and audio for COVID-19 related markers.
  • Human audio signals can be utilized for screening, diagnosis, and monitoring of COVID-19.
  • The integration of AI with audio bio-signals offers a pathway for non-invasive health assessment.

Conclusions:

  • AI-powered analysis of human audio signals presents a viable, non-intrusive approach for COVID-19 management.
  • Further development of automated systems using audio biomarkers can aid public health efforts.
  • Speech and non-speech audio analysis holds significant potential for future pandemic response.