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Related Experiment Video

Updated: Nov 19, 2025

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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Single-Channel Multiple-Receiver Sound Source Localization System with Homomorphic Deconvolution and Linear

Yeonseok Park1, Anthony Choi2, Keonwook Kim1

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

Sensors (Basel, Switzerland)
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a simplified single-channel sound localization system using multiple receivers for transportation applications. It achieves accurate angle-of-arrival prediction through homomorphic deconvolution and machine learning, reducing system complexity.

Keywords:
PronySteiglitz–McBrideYule–Walkerangle of arrivalcepstrumhomomorphic deconvolutionlinear regressionmachine learningsingle channelsound source localizationtime of flightvehicle

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Conventional sound source localization systems are complex due to multiple synchronized analog-to-digital conversion channels and scalable algorithms.
  • There is a need for simplified and efficient sound localization systems, particularly for transportation applications.

Purpose of the Study:

  • To propose and evaluate a single-channel sound localization system with multiple receivers.
  • To reduce the complexity of sound source localization systems for transportation.

Main Methods:

  • The proposed system utilizes a single analog microphone network connecting multiple receivers, transmitting a superimposed signal.
  • It employs a two-stage computational approach: homomorphic deconvolution for time-of-flight estimation and a machine learning stage (linear regression with supervised learning) for angle-of-arrival prediction.
  • Extensive simulations were performed to determine the optimal circular configuration for a three-receiver structure.

Main Results:

  • The Steiglitz-McBride parametric algorithm demonstrated the best performance in angle-of-arrival prediction among the tested homomorphic deconvolution methods.
  • The non-parametric method showed consistent performance, while the Yule-Walker parametric algorithm yielded the least accuracy.
  • Experiments in an anechoic chamber confirmed accurate predictions with appropriate ensemble length and model order.

Conclusions:

  • The proposed single-channel system offers a less complex alternative to conventional sound localization systems.
  • The combination of homomorphic deconvolution and supervised machine learning effectively predicts the angle of arrival.
  • The Steiglitz-McBride algorithm is recommended for its superior performance in this context.