Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 25, 2026

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners

Published on: March 13, 2026

Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks.

John C Murray¹, Harry R Erwin, Stefan Wermter

¹Hybrid Intelligent Systems, University of Sunderland, Sunderland, Tyne and Wear, SR6 0DD, United Kingdom.

Neural Networks : the Official Journal of the International Neural Network Society

|February 24, 2009

Summary

This study introduces a hybrid sound-source localization model for mobile robots in noisy environments. The system uses cross-correlation and recurrent neural networks for accurate acoustic source tracking, inspired by the mammalian central auditory system.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Will multimodal large language models ever achieve deep understanding of the world?

Frontiers in systems neuroscience·2025

Same author

Disentanglement of Prosody Representations via Diffusion Models and Scheduled Gradient Reversal.

IEEE transactions on neural networks and learning systems·2025

Same author

Improvement of Recalcitrant Folliculitis Decalvans With Tirzepatide: A Case Report.

Cureus·2025

Same author

Pain recognition and pain empathy from a human-centered AI perspective.

iScience·2024

Same author

Map-based experience replay: a memory-efficient solution to catastrophic forgetting in reinforcement learning.

Frontiers in neurorobotics·2023

Same author

A Trained Humanoid Robot can Perform Human-Like Crossmodal Social Attention and Conflict Resolution.

International journal of social robotics·2023

Area of Science:

Robotics
Acoustic Signal Processing
Artificial Intelligence

Background:

Acoustically cluttered environments pose challenges for mobile robot sound localization.
Existing methods often struggle with robustness and computational efficiency on resource-constrained platforms.
Inspiration from the mammalian central auditory system (CAS) offers a novel approach to auditory processing.

Purpose of the Study:

To develop a robust and accurate sound-source localization and tracking model for mobile service robots.
To create a computationally efficient model suitable for robots with limited processing power and physical size.
To investigate a hybrid approach combining signal processing and neural networks for enhanced auditory perception.

Main Methods:

A hybrid model integrating band-pass filtering and cross-correlation with recurrent neural networks (RNNs).

More Related Videos

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

Related Experiment Videos

Last Updated: Jun 25, 2026

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners

Published on: March 13, 2026

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Enhancing an Avian Sound Recognition Model's Detection Precision via Logistic Regression of Large Acoustic Datasets: A Case Study of the European Robin (Erithacus rubecula)

Published on: April 11, 2026

Development inspired by the principles of the mammalian central auditory system (CAS).

System validation through experimental testing in both controlled and real-world acoustic environments.

Main Results:

The proposed hybrid model demonstrates accurate and robust sound-source localization in cluttered acoustic settings.
The system is optimized for low power consumption and small physical size, enabling deployment on diverse robotic platforms.
Experimental results confirm the model's effectiveness in both restricted and real-world conditions.

Conclusions:

A hybrid architecture employing band-pass filtering, cross-correlation, and RNNs can achieve fast, accurate, and robust sound-source localization for mobile robots.
The model's design considerations address practical limitations of robotic systems, such as processing power and size.
The bio-inspired approach offers a promising direction for advanced robotic auditory perception.