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Compression of head-related transfer function using autoregressive-moving-average models and Legendre polynomials.

Sayedali Shekarchi1, John Hallam, Jakob Christensen-Dalsgaard

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The Journal of the Acoustical Society of America
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This study introduces a novel method to compress large head-related transfer function (HRTF) datasets using autoregressive-moving-average filters and Legendre polynomials. This technique achieves over 98% compression with minimal spectral error, enabling real-time applications.

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Area of Science:

  • Acoustics and Signal Processing
  • Computational Auditory Scene Analysis
  • Virtual Reality and Augmented Reality Audio

Background:

  • Head-related transfer functions (HRTFs) are essential for spatial audio rendering but generate large datasets.
  • Large HRTF datasets pose significant challenges for real-time embedded systems due to memory and processing constraints.
  • Efficient compression of HRTF data is crucial for widespread adoption in immersive audio technologies.

Purpose of the Study:

  • To develop and evaluate a novel compression method for head-related transfer function (HRTF) datasets.
  • To significantly reduce the data size of HRTFs while preserving audio fidelity for real-time applications.
  • To investigate the effectiveness of autoregressive-moving-average (ARMA) filters and Legendre polynomials (LPs) for HRTF compression.

Main Methods:

  • HRTFs were compressed by converting them into autoregressive-moving-average (ARMA) filters using Prony's method.
  • The coefficients of the ARMA filters were further compressed using Legendre polynomials (LPs) derived on the sphere.
  • The spatial complexity of HRTFs was assessed by the number of LPs required for accurate representation.

Main Results:

  • The proposed method achieved compression ratios exceeding 98% for HRTF datasets.
  • The spectral error in the recovered HRTFs was maintained below 4 dB.
  • The number of Legendre polynomials required correlated with the spatial complexity of the HRTF.

Conclusions:

  • The combined ARMA filter and Legendre polynomial approach offers highly effective compression for HRTF data.
  • This method significantly reduces HRTF dataset size, making them suitable for embedded real-time audio systems.
  • The technique preserves essential spatial audio characteristics with minimal loss of fidelity.