Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Physical Assessment of the Respiratory Tract IV: Auscultation

Physical Assessment of the Respiratory Tract IV: Auscultation

Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.

Detection of Gross Error: The Q Test

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

Respiratory System Abnormal Finding II: Palpation and Auscultation

Respiratory System Abnormal Finding II: Palpation and Auscultation

In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:

Perceiving Loudness, Pitch, and Location

Perceiving Loudness, Pitch, and Location

The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...

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

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 author

Explainable AI for Healthcare.

Studies in health technology and informatics·2026

Search research articles

Related Experiment Video

Updated: Aug 29, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Triplet Loss-Based Models for COVID-19 Detection from Vocal Sounds.

Adria Mallol-Ragolta, Florian B Pokorny, Katrin D Bartl-Pokorny

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|September 10, 2022

Summary

This study shows that analyzing vocal sounds like coughs and speech can help automatically detect COVID-19. Triplet loss models using speech data achieved the best results for COVID-19 detection.

More Related Videos

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

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

Published on: August 9, 2024

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

Related Experiment Videos

Last Updated: Aug 29, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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

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

Published on: August 9, 2024

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

Area of Science:

Medical acoustics
Artificial Intelligence in Healthcare
Signal Processing

Background:

The COVID-19 pandemic necessitated rapid diagnostic tools.
Vocal biomarkers offer a non-invasive method for disease detection.
Acoustic analysis of speech and coughs shows potential for identifying respiratory illnesses.

Purpose of the Study:

To investigate the efficacy of Mel-spectrogram analysis with Convolutional Neural Networks (CNNs) for automatic COVID-19 detection.
To evaluate the impact of triplet loss functions on improving the separability and robustness of deep learned representations for COVID-19 detection.
To assess the performance of these models using a dataset of German speakers recorded via smartphones.

Main Methods:

Utilized Mel-spectrograms as representations of vocal sounds (sustained vowels, coughs, reading speech).
Employed Convolutional Neural Networks (CNNs) for feature extraction from Mel-spectrograms.
Implemented a triplet loss function to enhance inter-class representation separability and robustness.
Conducted experiments on the 'Your Voice Counts' dataset.

Main Results:

Triplet loss-based models demonstrated suitability for COVID-19 detection from vocal sounds.
The best performance, an Unweighted Average Recall (UAR) of 66.5%, was achieved using a triplet loss model with speech data from reading.
The findings suggest that specific vocal tasks can yield more discriminative features for COVID-19 detection.

Conclusions:

Automatic detection of COVID-19 using vocal sound analysis is feasible.
Triplet loss functions can improve the performance of deep learning models for this task.
Speech recorded during reading tasks shows particular promise for COVID-19 detection via acoustic analysis.