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Related Experiment Video

Updated: Oct 10, 2025

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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Sound Source Localization Using a Convolutional Neural Network and Regression Model.

Tan-Hsu Tan1, Yu-Tang Lin1, Yang-Lang Chang1

  • 1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Convolutional Neural Network-Regression (CNN-R) model for precise sound source localization. The CNN-R model accurately estimates sound source angle and distance using interaural phase difference features.

Keywords:
convolutional neural networkdeep learningregression modelsound source localization

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Area of Science:

  • Acoustics and Signal Processing
  • Machine Learning for Audio Analysis

Background:

  • Accurate sound source localization is crucial for applications like robotics and augmented reality.
  • Existing methods often struggle with accuracy in complex acoustic environments.

Purpose of the Study:

  • To develop and evaluate a novel Convolutional Neural Network-Regression (CNN-R) model for sound source localization.
  • To estimate both the angle and distance of a sound source using acoustic features.

Main Methods:

  • Extraction of interaural phase difference (IPD) features from the time-frequency domain using short-time Fourier transform (STFT).
  • Utilizing a CNN-R model, treating IPD feature maps as images for localization.
  • Dataset generation using Pyroomacoustics and the MIRD database for simulated and real-world impulse responses.

Main Results:

  • Achieved high average accuracies: 98.96% for angle and 98.31% for distance in simulations (SNR=30 dB, RT60=0.16 s).
  • Demonstrated superior performance in real environments with average accuracies of 99.85% for angle and 99.38% for distance.
  • Outperformed existing sound source localization models in both simulated and real scenarios.

Conclusions:

  • The proposed CNN-R model offers a significant advancement in sound source localization accuracy.
  • The model's high performance indicates strong potential for practical, real-life applications.
  • This approach provides a robust solution for determining sound source position based on acoustic cues.