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A framework for designing head-related transfer function distance metrics that capture localization perception.

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This study introduces a machine learning framework to create a perceptual error metric for head-related transfer functions (HRTFs). This new metric aligns better with human sound localization than standard methods.

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Area of Science:

  • Acoustics
  • Machine Learning
  • Psychoacoustics

Background:

  • Linear comparisons of head-related transfer functions (HRTFs) inadequately capture perceptual differences.
  • This limitation hinders applications in perceptual testing, HRTF selection, and predictive modeling.

Purpose of the Study:

  • To develop a machine learning framework for a perceptual error metric for HRTFs.
  • To align this metric with human sound localization performance.

Main Methods:

  • A neural network was trained on a large HRTF database to predict measurement locations.
  • The network was subsequently fine-tuned using perceptual data.
  • A statistical test was used to assess information gain from perceptual observations.

Main Results:

  • The proposed perceptual error metric demonstrated robust performance.
  • It outperformed a standard spectral difference error metric.

Conclusions:

  • The developed machine learning framework provides a more perceptually aligned error metric for HRTFs.
  • This advancement improves the utility of HRTFs in various auditory research and application domains.