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Sound source localization using multiple ad hoc distributed microphone arrays.

Manuel Hahmann1, Efren Fernandez-Grande2, Henrry Gunawan1

  • 1Noiselab, Marine Physical Laboratory, University of California San Diego, La Jolla, California 92037, USA.

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Summary
This summary is machine-generated.

This study introduces an affine mapping method for sound source localization using distributed microphone arrays. The approach accurately estimates sound source positions in three dimensions, improving upon existing techniques.

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Area of Science:

  • Acoustics and Signal Processing
  • Computational Audition
  • Robotics and Sensor Networks

Background:

  • Accurate sound source localization is essential for human communication and understanding complex acoustic environments.
  • Distributed microphone arrays offer a flexible approach to capturing sound from multiple perspectives.
  • Existing localization methods face challenges with accuracy, especially in reverberant or complex spaces.

Purpose of the Study:

  • To develop and validate an affine mapping method for sound source localization using direction-of-arrival estimates from multiple ad hoc microphone arrays.
  • To assess the sufficiency and calibration capabilities of the affine model for determining source coordinates in a room.
  • To compare the performance of the affine localization approach against a nonlinear neural network in three-dimensional space.

Main Methods:

  • Utilizing direction-of-arrival (DOA) estimates from multiple, independently operating microphone arrays.
  • Deriving an affine mapping model based on a set of calibration points to relate array DOA estimates to source coordinates.
  • Implementing a projection technique to enhance local array estimates and improve localization accuracy, particularly in uncalibrated regions.
  • Conducting three-dimensional localization experiments to compare the affine method with a nonlinear neural network approach.

Main Results:

  • The derived affine mapping model proved sufficient for accurate sound source localization.
  • The affine model demonstrated effective calibration to physical room dimensions.
  • Projecting local array estimates significantly increased localization accuracy, especially for sources distant from calibration points.
  • The affine approach showed competitive performance against a nonlinear neural network in three-dimensional localization tests.

Conclusions:

  • An affine mapping provides a robust and calibratable method for sound source localization using distributed microphone arrays.
  • The proposed technique enhances localization accuracy through projection, extending its effectiveness beyond calibrated areas.
  • The affine model offers a viable alternative to more complex methods like neural networks for practical sound source localization applications.