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

Multiple Voltage Sources01:25

Multiple Voltage Sources

1.8K
Generally, a single battery is not enough to power some devices. In such cases, batteries can be combined in two ways: in series or in parallel.
In series, the positive terminal of one battery is connected to the negative terminal of another battery. Hence, the voltage of each battery is added to give the net voltage, which is increased because each battery boosts the electrons that enter it. The same current flows through each battery because they are connected in series.
Batteries are...
1.8K
Soundness of Cement01:17

Soundness of Cement

559
The soundness of cement refers to the ability of cement paste to retain its volume after setting. Unsound cement can lead to expansion and structural damage due to the presence of free lime, magnesia, and calcium sulfate. Free lime hydrates very slowly, expanding and causing unsoundness, which is difficult to detect because it intercrystallizes with other compounds. Magnesia also reacts with water, forming crystals that can disrupt the cement's structure. Calcium sulfate can create...
559
Heart Sounds01:15

Heart Sounds

3.5K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
3.5K
Korotkoff Sounds01:12

Korotkoff Sounds

7.9K
Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
During blood pressure assessment, inflating the cuff 30 millimeters of mercury above the patient's systolic blood pressure...
7.9K
Sound Waves01:01

Sound Waves

12.9K
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....
12.9K
Sound Intensity00:58

Sound Intensity

4.8K
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...
4.8K

You might also read

Related Articles

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

Sort by
Same author

Erratum: Estimating a robustness increase in spherical harmonic transforms resulting from oversampling on a sphere [J. Acoust. Soc. Am. 158(4), 3619-3630 (2025)].

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

Estimating a robustness increase in spherical harmonic transforms resulting from oversampling on a sphere.

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

Sound field estimation for source-included region based on Gaussian process using prior source information.

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

Spatially characterized pseudo-perfect diffuseness via finite-degree spherical harmonic diffuseness.

JASA express letters·2024

Related Experiment Video

Updated: Jan 28, 2026

Preparing a Celadonite Electron Source and Estimating Its Brightness
09:14

Preparing a Celadonite Electron Source and Estimating Its Brightness

Published on: November 5, 2019

4.9K

Sparsity based inhomogeneous sound field estimation using multiple source-regions kernel.

Ryo Matsuda1, Makoto Otani1

  • 1Department of Architecture and Architectural Engineering, Graduate School of Engineering, Kyoto University, Kyoto-daigaku-katsura, Nishikyo-ku, Kyoto 615-8540, Japan.

The Journal of the Acoustical Society of America
|January 27, 2026
PubMed
Summary

This study introduces a novel sparse method for estimating complex sound fields using microphone arrays. The approach enhances accuracy by modeling spatial correlations and optimizing source region parameters.

More Related Videos

A Quantitative Fluorescence Microscopy-based Single Liposome Assay for Detecting the Compositional Inhomogeneity Between Individual Liposomes
09:12

A Quantitative Fluorescence Microscopy-based Single Liposome Assay for Detecting the Compositional Inhomogeneity Between Individual Liposomes

Published on: December 13, 2019

8.4K
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K

Related Experiment Videos

Last Updated: Jan 28, 2026

Preparing a Celadonite Electron Source and Estimating Its Brightness
09:14

Preparing a Celadonite Electron Source and Estimating Its Brightness

Published on: November 5, 2019

4.9K
A Quantitative Fluorescence Microscopy-based Single Liposome Assay for Detecting the Compositional Inhomogeneity Between Individual Liposomes
09:12

A Quantitative Fluorescence Microscopy-based Single Liposome Assay for Detecting the Compositional Inhomogeneity Between Individual Liposomes

Published on: December 13, 2019

8.4K
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.7K

Area of Science:

  • Acoustics
  • Signal Processing
  • Computational Physics

Background:

  • Estimating inhomogeneous sound fields from microphone array data is crucial for applications like acoustic source localization and sound field analysis.
  • Conventional methods often rely on simplifying assumptions, such as point-source approximations, which can limit accuracy in complex acoustic environments.

Purpose of the Study:

  • To develop a more accurate method for estimating inhomogeneous sound fields using microphone array signals.
  • To leverage the sparsity of sound source distributions for improved estimation.
  • To propose a flexible kernel-based approach that can adapt to various source region characteristics.

Main Methods:

  • A kernel function is defined as a weighted sum of sub-kernels, each characterizing spatial correlations within defined spherical source regions.
  • A gradient-based algorithm is employed to iteratively update the kernel weights, exploiting the inherent sparsity of the source distribution.
  • A novel scheme is introduced to update kernel parameters (radii and centers of source regions) using derived analytical gradient expressions.

Main Results:

  • Theoretical analysis establishes a connection between the proposed method and traditional sparse point-source decomposition techniques.
  • Numerical simulations confirm that the proposed method achieves superior estimation accuracy compared to existing conventional approaches.
  • The method effectively handles the complexities of inhomogeneous sound fields by adapting its kernel representation.

Conclusions:

  • The proposed sparse, kernel-based method offers a significant advancement in estimating inhomogeneous sound fields from microphone array data.
  • The flexibility in defining and updating kernel parameters allows for robust performance across diverse acoustic scenarios.
  • This approach provides a more accurate and adaptable alternative to conventional methods, particularly in scenarios with complex source distributions.