Jove
Visualize
Contact Us

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

669
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
669
Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

892
As with waves on a string, the speed of sound or a mechanical wave in a fluid depends on the fluid's elastic modulus and inertia. The two relevant physical quantities are the bulk modulus and the density of the material. Indeed, it turns out that the relationship between speed and the bulk modulus and density in fluids is the same as that between the speed and the Young's modulus and density in solids.
The speed of sound in fluids can be derived by considering a mechanical wave...
892
Sound Waves01:01

Sound Waves

12.4K
Sound waves can be thought of as fluctuations in the pressure of a medium through which they propagate. Since the pressure also makes the medium's particles vibrate along its direction of motion, the waves can be modeled as the displacement of the medium's particles from their mean position.
Sound waves are longitudinal in most fluids because fluids cannot sustain any lateral pressure. In solids, however, shear forces help in propagating the disturbance in the lateral direction as well....
12.4K
Sound Waves: Interference00:53

Sound Waves: Interference

4.5K
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...
4.5K
Perception of Sound Waves01:01

Perception of Sound Waves

5.4K
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...
5.4K
Echo01:06

Echo

869
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,...
869

You might also read

Related Articles

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

Sort by
Same author

Continuous forecasting of range-dependent ocean sound speed field: Diffusion model meets multi-output Gaussian process.

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

Sensor beampattern and equivalent aperture in a distributed acoustic sensing system.

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

Reverberation-chamber measurement of angle-dependent surface impedancea).

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

Hankel-FNO: Fast underwater acoustic charting via physics-encoded Fourier neural operator.

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

Directional sound field decay analysis in a reverberation rooma).

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

Evaluating Gaussian processes for matched-field processing localization using minimum mean squared error criterion.

JASA express letters·2025
Same journal

Reducing computational complexity in adaptive sound zones with online room impulse response estimation.

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

Small-sample unbiased linear coherence estimators for a complex Gaussian random process.

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

Automated detection and annotation of toothed-whale whistles using transformer-based instance segmentation.

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

Effect of temperature and concentration on the thermo-acoustic behavior of vitamin B5 (d-Panthenol) solutions in the presence of glycol additives.

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

The visome: Using cognitive networks to examine lip-reading errors in English words.

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

Resident subjective annoyance responses to combined road traffic and train-induced structure-borne noise: Effects of sound environment.

The Journal of the Acoustical Society of America·2026
See all related articles
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
  1. Home
  2. Differentiable Physics For Sound Field Reconstruction.
  1. Home
  2. Differentiable Physics For Sound Field Reconstruction.

Related Experiment Video

Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces
10:21

Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces

Published on: July 26, 2016

12.0K

Differentiable physics for sound field reconstruction.

Samuel A Verburg1, Efren Fernandez-Grande2, Peter Gerstoft1

  • 1Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), Kongens Lyngby, Denmark.

The Journal of the Acoustical Society of America
|November 21, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study presents a novel differentiable physics approach for sound field reconstruction. It enables accurate sound field estimation even with limited data, outperforming traditional physics-informed neural networks.

More Related Videos

A Method to Study Adaptation to Left-Right Reversed Audition
07:14

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

6.8K
Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
04:54

Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging

Published on: June 16, 2023

3.7K

Related Experiment Videos

Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces
10:21

Evanescent Field Based Photoacoustics: Optical Property Evaluation at Surfaces

Published on: July 26, 2016

12.0K
A Method to Study Adaptation to Left-Right Reversed Audition
07:14

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

6.8K
Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
04:54

Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging

Published on: June 16, 2023

3.7K

Area of Science:

  • Acoustics
  • Computational Physics
  • Machine Learning

Background:

  • Sound field reconstruction estimates sound fields from limited spatial observations.
  • Conventional methods like physics-informed neural networks (PINNs) incorporate physics into the loss function.
  • Severe undersampling poses challenges for accurate sound field reconstruction.

Purpose of the Study:

  • To introduce a differentiable physics approach for robust sound field reconstruction.
  • To enhance accuracy and convergence under data-scarce conditions.
  • To enforce physics as a strong constraint during network training.

Main Methods:

  • Approximating wave equation initial conditions with a neural network.
  • Utilizing a differentiable numerical solver for the differential operator.
  • Incorporating a sparsity-promoting constraint for improved reconstruction.
  • Main Results:

    • The proposed method achieves stable network training by enforcing physics as a strong constraint.
    • Demonstrated successful sound field reconstruction under extreme data scarcity.
    • Outperformed conventional physics-informed neural networks in accuracy and convergence.

    Conclusions:

    • The differentiable physics approach offers a stable and effective method for sound field reconstruction.
    • This technique significantly improves performance in undersampled scenarios.
    • It provides a strong alternative to existing physics-informed neural network methods.