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

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

1.3K
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...
1.3K
Sound Intensity Level00:53

Sound Intensity Level

4.0K
Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and...
4.0K
Perception of Sound Waves01:01

Perception of Sound Waves

4.7K
The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
4.7K
Bandpass Sampling01:17

Bandpass Sampling

681
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
681
Sound Intensity00:58

Sound Intensity

4.1K
The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
4.1K
Intensity and Pressure of Sound Waves01:05

Intensity and Pressure of Sound Waves

1.9K
The intensity of sound waves can be related to displacement and pressure amplitudes by using their wave expressions and the definition of intensity. The critical step to achieve this is to write the power delivered by the particles on the wave as the product of force and velocity and simplify the force per unit area as the pressure. The velocity of the medium's particles can be derived from the displacement.
Unlike the time average of a sinusoidal term, which is zero since it is positive...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Background noise inhibits listeners' use of contextual cues for dysarthric speech.

JASA express letters·2026
Same author

Communication in Complex Situations: The Combined Influence of Dysarthria and Sensorineural Hearing Loss on Speech Perception in Everyday Noisy Environments.

Journal of speech, language, and hearing research : JSLHR·2025
Same author

Perceptual effects of reducing algorithmic latency on deep-learning based noise reductiona).

The Journal of the Acoustical Society of America·2025
Same author

An ideal compressed mask for increasing speech intelligibility without sacrificing environmental sound recognitiona).

The Journal of the Acoustical Society of America·2024
Same author

The Optimal Speech-to-Background Ratio for Balancing Speech Recognition With Environmental Sound Recognition.

Ear and hearing·2024
Same author

Progress made in the efficacy and viability of deep-learning-based noise reduction.

The Journal of the Acoustical Society of America·2023

Related Experiment Video

Updated: May 2, 2026

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

1.2K

Estimating band importance for environmental sound recognition using deep learninga).

Eric M Johnson1, Eric W Healy2

  • 1Division of Communication Sciences and Disorders, and West Virginia Clinical and Translational Science Institute, West Virginia University, Morgantown, West Virginia 26506 USA.

The Journal of the Acoustical Society of America
|May 1, 2026
PubMed
Summary
This summary is machine-generated.

This study used deep learning to identify crucial frequency bands for environmental sound recognition (ESR) amidst speech. Findings reveal human performance aligns with task-optimal frequencies for sound segregation.

Related Experiment Videos

Last Updated: May 2, 2026

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

1.2K

Area of Science:

  • Auditory Neuroscience
  • Machine Learning
  • Acoustic Signal Processing

Background:

  • Environmental sound recognition (ESR) is vital for interpreting complex auditory scenes.
  • Understanding the specific frequency regions critical for ESR is limited.
  • Speech interference poses a significant challenge to accurate sound recognition.

Purpose of the Study:

  • To model ESR using deep learning and estimate frequency band-importance functions (BIFs).
  • To compare BIFs derived from human performance versus task-optimal models.
  • To identify key frequency bands supporting environmental sound identification in noisy conditions.

Main Methods:

  • Collected listener responses for identifying everyday sounds mixed with speech across varying signal-to-noise ratios.
  • Developed two deep learning models: one mimicking human performance (soft labels) and one optimized for accuracy (ground-truth labels).
  • Estimated BIFs by bandstop filtering and measuring the impact on recognition accuracy.

Main Results:

  • Both human-trained and ground-truth-trained models produced reproducible BIFs with five distinct peaks (∼0.43, 0.77, 1.46, 2.6, 9.7 kHz).
  • The ground-truth model surpassed human accuracy, demonstrating high reliability.
  • Human performance closely mirrored the task-optimal frequency patterns identified by the models.

Conclusions:

  • Human environmental sound recognition performance appears to leverage task-optimal frequency bands.
  • Deep learning models effectively identify critical spectral regions for sound segregation from speech.
  • The convergence of results suggests efficient use of spectral information by the human auditory system.