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Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System.

Yeonseok Park1, Anthony Choi2, Keonwook Kim1

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

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Summary

This study introduces a novel sound localization system using a single analog microphone network. It accurately estimates flight times between microphones, crucial for vehicle-mounted sound source detection and future AI applications.

Keywords:
PronySteiglitz-McBrideYule-walkercepstrumhomomorphic deconvolutionsound source localizationtime of flightvehicle

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Area of Science:

  • Engineering
  • Acoustics
  • Signal Processing

Background:

  • Vehicle-mounted sound source localization systems aid driving by monitoring surroundings.
  • Challenges exist in omnidirectional localization due to vehicle 3D structure and signal propagation complexities.

Purpose of the Study:

  • To propose a novel sound localization system utilizing a single analog microphone network.
  • To investigate flight time estimation methods for accurate sound source localization.

Main Methods:

  • Flight time estimation using non-parametric homomorphic deconvolution for two microphones.
  • Parametric methods including Yule-Walker, Prony, and Steiglitz-McBride algorithms for propagation model coefficient derivation.

Main Results:

  • Non-parametric and Steiglitz-McBride methods showed low bias and variance with ensemble lengths of 20 or more.
  • Yule-Walker and Prony algorithms demonstrated improved statistical performance with increased ensemble averaging.
  • Both non-parametric and parametric homomorphic deconvolution effectively represent flight time information.

Conclusions:

  • Homomorphic deconvolution methods, both non-parametric and parametric, are effective for flight time estimation in sound localization.
  • The derived flight time information serves as key features for future machine learning and deep learning-based localization systems.