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Three-Dimensional Sound Source Localization with Microphone Array Combining Spatial Entropy Quantification and

Guangneng Li1, Feiyu Zhao1,2, Wei Tian1

  • 1College of Computer Science, South-Central Minzu University, Wuhan 430071, China.

Entropy (Basel, Switzerland)
|September 27, 2025
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Summary
This summary is machine-generated.

This study introduces a novel eight-microphone system for 3D sound source localization (SSL). The method accurately positions multiple sound sources in complex environments, achieving a positioning error of approximately 10.0 cm.

Keywords:
acoustic detectionmachine learningsound source localization (SSL)spatial entropy

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Intelligent scene monitoring relies heavily on sound source localization (SSL) for indoor positioning.
  • Existing SSL methods struggle with multi-source and three-dimensional (3D) environments.
  • There is a need for robust SSL techniques applicable to complex 3D scenarios.

Purpose of the Study:

  • To develop an accurate 3D sound source localization technology using an eight-microphone array.
  • To address the limitations of traditional SSL methods in multi-source and 3D environments.
  • To improve the precision of sound source positioning in intelligent monitoring systems.

Main Methods:

  • Utilized a rectangular eight-microphone array to capture Direction-of-Arrival (DOA) information via the direct path relative transfer function (DP-RTF).
  • Introduced spatial entropy to quantify DOA combination uncertainty and geometric intersection to reduce it for multi-source scenarios.
  • Employed machine learning to correct coordinate deviations stemming from DOA estimation errors and microphone parameter inaccuracies.

Main Results:

  • The proposed method effectively localizes sound sources in 3D space, overcoming limitations of traditional approaches.
  • Spatial entropy reduction and geometric intersection significantly improved multi-source localization accuracy.
  • Machine learning integration minimized positioning errors caused by DOA estimation and microphone calibration deviations.
  • Achieved a positioning error of approximately 10.0 cm in both simulated and real-world experiments.

Conclusions:

  • The developed eight-microphone-based 3D SSL technology offers a significant advancement for intelligent monitoring.
  • The integration of spatial entropy, geometric intersection, and machine learning provides a robust solution for complex acoustic environments.
  • This method demonstrates high accuracy and applicability in real-world 3D sound source localization tasks.