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

Echo01:06

Echo

1.0K
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,...
1.0K
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

1.1K
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...
1.1K
Sound as Pressure Waves01:17

Sound as Pressure Waves

4.6K
Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
4.6K

You might also read

Related Articles

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

Sort by
Same author

A performance metric for screen selection with the acoustic single pixel imager.

The Journal of the Acoustical Society of America·2018
Same author

Exploiting platform motion for passive source localization with a co-prime sampled large aperture array.

The Journal of the Acoustical Society of America·2018
Same author

Beamforming using chip-scale atomic clocks in a controlled environment.

The Journal of the Acoustical Society of America·2018
Same author

Maximum-likelihood spatial spectrum estimation in dynamic environments with a short maneuverable array.

The Journal of the Acoustical Society of America·2013
Same author

Time-varying spatial spectrum estimation with a maneuverable towed array.

The Journal of the Acoustical Society of America·2011
Same author

Computational prediction and experimental verification of new MAP kinase docking sites and substrates including Gli transcription factors.

PLoS computational biology·2010

Related Experiment Video

Updated: Feb 17, 2026

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

3.8K

Ambient noise forecasting with a large acoustic array in a complex shallow water environment.

Jeffrey S Rogers1, Stephen C Wales1, Steven L Means1

  • 1United States Naval Research Laboratory, Code 7160, Washington, DC 20375, USA jeff.rogers@nrl.navy.mil, scwales@aol.com, slm135@gmail.com.

The Journal of the Acoustical Society of America
|December 3, 2017
PubMed
Summary

Accurate ocean ambient noise forecasting improves sonar detection performance. This method uses known ship positions and acoustic data to predict noise levels, achieving a low mean-squared error of 3.5 dB.

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
Design and Construction of a Cost Effective Headstage for Simultaneous Neural Stimulation and Recording in the Water Maze
09:09

Design and Construction of a Cost Effective Headstage for Simultaneous Neural Stimulation and Recording in the Water Maze

Published on: October 13, 2010

11.1K

Related Experiment Videos

Last Updated: Feb 17, 2026

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

3.8K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
Design and Construction of a Cost Effective Headstage for Simultaneous Neural Stimulation and Recording in the Water Maze
09:09

Design and Construction of a Cost Effective Headstage for Simultaneous Neural Stimulation and Recording in the Water Maze

Published on: October 13, 2010

11.1K

Area of Science:

  • Oceanography
  • Acoustics
  • Signal Processing

Background:

  • Ocean ambient noise forecasting is crucial for sonar system performance characterization.
  • Accurate noise prediction enhances operational capabilities and future performance projections.
  • Existing methods can be improved with precise source localization and bearing resolution.

Purpose of the Study:

  • To enhance ocean ambient noise forecasting using a priori source position knowledge.
  • To validate the effectiveness of a novel forecasting approach with real-world data.
  • To quantify the accuracy of the proposed noise forecasting method.

Main Methods:

  • Utilized radar and Automatic Identification System (AIS) for source positioning.
  • Computed transmission loss (TL) from known source positions to a large aperture research array.
  • Estimated individual ship source levels (SLs) using non-negative least squares and array beam response.
  • Generated ambient noise forecasts by projecting estimated SLs along ship tracks.

Main Results:

  • Successfully estimated individual ship source levels (SLs).
  • Developed a method for projecting estimated SLs along known ship tracks to form noise forecasts.
  • Achieved a mean-squared error as low as 3.5 dB in 30-minute ambient noise forecast estimates.
  • Demonstrated the effectiveness of integrating source position data into noise forecasting.

Conclusions:

  • The integration of precise source positions and bearing resolution significantly improves ocean ambient noise forecasting.
  • The developed method provides accurate short-term (30-minute) ambient noise predictions.
  • This approach offers a valuable tool for characterizing and projecting sonar system performance in dynamic ocean environments.