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A spatial compression technique for head-related transfer function interpolation and complexity estimation.

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Legendre polynomials (LPs) effectively indicate head-related transfer function (HRTF) spatial complexity. LP compression efficiency correlates with HRTF complexity, predicting interpolation accuracy in spatial audio.

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Area of Science:

  • Acoustics and Audio Engineering
  • Signal Processing
  • Virtual Reality and Spatial Audio

Background:

  • Head-related transfer functions (HRTFs) are crucial for realistic 3D audio rendering.
  • Compressing HRTF datasets is essential for efficient storage and real-time applications.
  • Existing HRTF modeling techniques often struggle with capturing spatial complexity accurately.

Purpose of the Study:

  • To evaluate Legendre polynomials (LPs) as a method for assessing HRTF spatial complexity.
  • To investigate the efficacy of LP-based compression for HRTF datasets.
  • To establish a relationship between HRTF complexity, LP compressibility, and interpolation error.

Main Methods:

  • Applied Legendre polynomial (LP) compression to diverse real and synthetic HRTF datasets.
  • Quantitatively defined an HRTF spatial complexity index based on power spectrum changes.
  • Analyzed the correlation between LP compressibility, dataset complexity, and spatial resolution.

Main Results:

  • LP compression effectiveness is independent of the number of spatial samples in the HRTF dataset.
  • HRTF compressibility directly correlates with the defined HRTF spatial complexity index.
  • Higher complexity HRTFs require more LP coefficients for accurate representation.

Conclusions:

  • Legendre polynomials serve as a robust indicator of HRTF spatial complexity.
  • The LP compression technique effectively models HRTFs, with compressibility linked to complexity.
  • The rate of change in the complexity index predicts potential high interpolation errors.