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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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Related Experiment Video

Updated: May 5, 2026

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners
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Published on: March 13, 2026

184

End-to-End 3-D Sound Source Localization from the Raw Waveform Based on Stereo Microphone Array.

Lipeng Xu1, Chao Yang1

  • 1School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201600, China.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

This study introduces a novel artificial neural network (ANN) for 3-D sound source localization using raw audio. The method significantly improves accuracy in noisy and reverberant conditions, outperforming existing algorithms.

Keywords:
attention mechanismconvolutional neural networksend-to-endresidual connectionsound source localizationtetrahedral stereo microphone array

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Area of Science:

  • Acoustics
  • Artificial Intelligence
  • Signal Processing

Background:

  • Current sound source localization algorithms struggle with performance degradation in reverberant and noisy environments.
  • Existing methods often rely on handcrafted features, limiting adaptability and accuracy.

Purpose of the Study:

  • To develop a novel artificial neural network (ANN) approach for direct 3-D sound source localization from raw audio signals.
  • To enhance the robustness and accuracy of sound source localization systems in challenging acoustic conditions.

Main Methods:

  • Utilized a tetrahedral stereo microphone array to capture raw audio signals.
  • Employed an ANN architecture incorporating convolutional layers for frequency analysis, residual connections (RC) for stability, and squeeze-and-excitation (SE) attention mechanisms for enhanced cue detection.
  • Processed raw audio directly, bypassing the need for handcrafted features and addressing spatial symmetry issues.

Main Results:

  • Achieved significant reductions in localization errors: 0.2 m in semi-anechoic chambers and 0.26 m in conference rooms.
  • Demonstrated substantial accuracy gains: 10% in semi-anechoic chambers and 21% in conference rooms.
  • The ANN approach proved effective in improving resistance to reverberation and noise.

Conclusions:

  • The proposed ANN-based method offers a robust and accurate solution for 3-D sound source localization.
  • Direct processing of raw audio signals with advanced network components enhances performance in adverse acoustic environments.
  • The findings validate the effectiveness of the approach in overcoming limitations of traditional sound source localization techniques.