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

Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

498
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
498
Methods of Medium Optimization01:28

Methods of Medium Optimization

45
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
45
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

432
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
432
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
Deriving the Speed of Sound in a Liquid01:09

Deriving the Speed of Sound in a Liquid

1.0K
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...
1.0K
Cluster Sampling Method01:20

Cluster Sampling Method

15.5K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.5K

You might also read

Related Articles

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

Sort by
Same author

Quantum lattice Boltzmann method for several time steps: A local Carleman linearization algorithm.

Physical review. E·2026
Same author

Flow-induced random guided wave on an airfoil with application to passive structural health monitoring: A numerical study via an LES-SEM scheme.

Ultrasonics·2026
Same author

Range-dependent shallow water sound source localization via digital twin model incorporating spectral element full-wave numerical simulation.

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

A new correlation model for ultrasonic attenuation in polycrystals with broad grain size distributions.

Ultrasonics·2025
Same author

Optimizing nanoparticle design for selective targeting of breast cancer cells.

Medical engineering & physics·2025
Same author

Adaptive lattice-gas algorithm: Classical and quantum implementations.

Physical review. E·2025

Related Experiment Video

Updated: Mar 28, 2026

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

Published on: December 20, 2024

984

Sound source localization in a randomly inhomogeneous medium using matched statistical moment method.

Xun Wang1, Shahram Khazaie1, Pierre Sagaut1

  • 1Aix-Marseille Université, CNRS, Centrale Marseille, M2P2 UMR 7340, 13451 Marseille, France.

The Journal of the Acoustical Society of America
|January 3, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical method for sound source localization in complex, randomly varying environments. The approach accurately estimates source location by analyzing measurement moments, overcoming limitations of traditional techniques.

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K

Related Experiment Videos

Last Updated: Mar 28, 2026

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention

Published on: December 20, 2024

984
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K

Area of Science:

  • Acoustics
  • Signal Processing
  • Statistical Physics

Background:

  • Traditional sound source localization methods fail in randomly inhomogeneous media.
  • Accurate localization is crucial for applications in noise control, robotics, and surveillance.
  • Random media introduce significant challenges to wave propagation analysis.

Purpose of the Study:

  • To develop a robust sound source localization method for randomly inhomogeneous media.
  • To address the limitations of conventional beamforming and holography techniques.
  • To propose a novel approach utilizing statistical moments of acoustical measurements.

Main Methods:

  • Modeling the random medium using Karhunen-Loève expansion for dimensionality reduction.
  • Analyzing statistical characteristics of acoustical measurements, including probability density functions and moments.
  • Localizing the sound source by minimizing the discrepancy between measured and simulated statistical moments.

Main Results:

  • The random medium can be effectively represented by a limited set of random variables.
  • Statistical moments of measurements ensure convergence of the localization algorithm.
  • Accurate estimation of sound source location was achieved using the proposed method.

Conclusions:

  • The proposed statistical moment-based approach offers a viable solution for sound source localization in challenging random environments.
  • Karhunen-Loève expansion effectively simplifies the representation of random propagation media.
  • The method demonstrates high accuracy and robustness, validated by numerical examples.