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

¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

2.2K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
2.2K
Sound Waves: Interference00:53

Sound Waves: Interference

5.3K
Sound waves can be modeled either as longitudinal waves, wherein the molecules of the medium oscillate around an equilibrium position, or as pressure waves. When two identical waves from the same source superimpose on each other, the combination of two crests or two troughs results in amplitude reinforcement known as constructive interference. If two identical waves, that are initially in phase, become out of phase because of different path lengths, the combination of crests with troughs...
5.3K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

2.4K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
2.4K
Perception of Sound Waves01:01

Perception of Sound Waves

6.1K
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...
6.1K
Echo01:06

Echo

1.3K
The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...
1.3K
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

1.4K
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...
1.4K

You might also read

Related Articles

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

Sort by
Same author

BioNet-A: Ultrasonic echo representation network for target discrimination using active SONAR.

The Journal of the Acoustical Society of America·2026
Same author

Correction: Premotor cortex hemodynamic responses primarily reflect perceptual rather than specific motor aspects of decision making.

PLoS biology·2026
Same author

A Biomimetic Microfluidic Triple-compartment Periodontium-on-chip for Investigation of Inflammatory Responses in Periodontitis.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Dissociation in cross-feature integration between behavioral and pupil dilation responses in auditory deviant detection.

iScience·2026
Same author

Object and setting identification in natural auditory scenesa).

The Journal of the Acoustical Society of America·2026
Same author

The shape of attention reflects flexible filtering of natural speech modulations.

Communications biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Apr 19, 2026

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

17.1K

Segregating complex sound sources through temporal coherence.

Lakshmi Krishnan1, Mounya Elhilali2, Shihab Shamma3

  • 1Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America.

Plos Computational Biology
|December 19, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for separating single-channel sound mixtures using temporal coherence and auditory cortex models. The approach clusters neural responses to isolate target sounds without prior training, offering insights into auditory perception.

More Related Videos

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.4K
Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
06:42

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Published on: January 19, 2019

11.3K

Related Experiment Videos

Last Updated: Apr 19, 2026

Infant Auditory Processing and Event-related Brain Oscillations
06:34

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

17.1K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.4K
Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
06:42

Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

Published on: January 19, 2019

11.3K

Area of Science:

  • Auditory Neuroscience
  • Computational Auditory Scene Analysis

Background:

  • Segregating sound sources in complex auditory environments is a fundamental perceptual challenge.
  • Existing methods often require prior information or extensive training.

Purpose of the Study:

  • To present a new algorithm for monaural sound mixture segregation.
  • To leverage temporal coherence and auditory cortical representations for source separation.
  • To explore the integration of cognitive functions like attention and memory into the segregation process.

Main Methods:

  • Utilizing the principle of temporal coherence, where sound sources emit correlated features.
  • Clustering neural response patterns that exhibit high coincidence.
  • Reconstructing input signals based on clustered features to isolate sources.
  • Developing a model that does not require prior source information or training.

Main Results:

  • Demonstrated a novel computational approach for sound segregation based on neural principles.
  • Showcased the algorithm's ability to separate target sources from interfering signals.
  • Highlighted the model's flexibility in incorporating cognitive influences like attention and memory.

Conclusions:

  • The proposed method offers a biologically plausible mechanism for auditory scene analysis.
  • The model provides testable hypotheses for the neural and psychophysical basis of sound segregation.
  • This approach advances our understanding of how the brain navigates complex auditory environments.