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

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

121
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....
121
Deconvolution01:20

Deconvolution

220
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
220
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

113
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
113
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

315
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
315
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

323
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...
323
Sinusoidal Sources01:18

Sinusoidal Sources

592
Direct current (DC) refers to an electric current that flows in a single direction, maintaining a constant polarity. This is in contrast to alternating current (AC), which periodically changes its direction and magnitude. AC forms the backbone of modern electricity transmission and distribution systems due to its efficient long-distance transmission capabilities.
In homes, the power supplies use sinusoidal sources to provide electricity. These sources generate a voltage that varies sinusoidally...
592

You might also read

Related Articles

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

Sort by
Same author

Single-Channel Multiple-Receiver Sound Source Localization System with Homomorphic Deconvolution and Linear Regression.

Sensors (Basel, Switzerland)·2021
Same author

Monaural Sound Localization Based on Reflective Structure and Homomorphic Deconvolution.

Sensors (Basel, Switzerland)·2017
See all related articles

Related Experiment Video

Updated: Aug 13, 2025

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

373

Gaussian Process Regression for Single-Channel Sound Source Localization System Based on Homomorphic Deconvolution.

Keonwook Kim1, Yujin Hong1

  • 1Division of Electronics & Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved single-channel sound source localization system using Gaussian process regression. The novel method enhances prediction accuracy for sound source localization, simplifying complex systems.

Keywords:
Gaussian process regressionPronySteiglitz–McBrideYule–Walkerangle of arrivalcepstrumhomomorphic deconvolutionmachine learningsimilarity matrixsingle channelsound source localizationtime of flight

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

14.7K
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

6.6K

Related Experiment Videos

Last Updated: Aug 13, 2025

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

373
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

14.7K
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

6.6K

Area of Science:

  • Acoustics and Signal Processing
  • Machine Learning Applications

Background:

  • Conventional sound source localization systems are complex in hardware and software, limiting scalability.
  • Existing single-channel systems aggregate receivers but require algorithmic improvements for enhanced capability.

Purpose of the Study:

  • To propose an improved algorithm for single-channel sound source localization.
  • To enhance prediction accuracy and system capability by simplifying hardware and software requirements.

Main Methods:

  • A novel feature extraction method combined with Gaussian process regression.
  • A three-stage computational process: homomorphic deconvolution, feature extraction, and Gaussian process regression.
  • Similarity matrix analysis for optimal receiver configuration in a three-receiver setup.

Main Results:

  • Precise sound source localization predictions demonstrated through simulations and experiments.
  • The nonparametric Gaussian process regression with a rational quadratic kernel showed consistent performance.
  • The Steiglitz-McBride model with an exponential kernel yielded the best predictions for both trained and untrained angles, exhibiting low bias and variance.

Conclusions:

  • The proposed improved single-channel sound source localization system offers high accuracy and simplified architecture.
  • Gaussian process regression, particularly with the Steiglitz-McBride model and exponential kernel, is effective for robust sound source localization.
  • The novel feature extraction and similarity matrix analysis contribute to optimized system performance.