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Distance-Dependent Modeling of Head-Related Transfer Functions Based on Spherical Fourier-Bessel Transform

  • Xiaoke Qi
  • Jianhua Tao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 807)

Abstract

Spherical harmonic (SH)-based methods have been proposed for modeling head-related transfer functions (HRTFs) and yielded an encouraging performance level in terms of log-spectral distortion (LSD). However, most of these techniques model HRTFs on a sphere, and rarely exploit the correlation relationship of HRTFs from different distances, and as a consequence HRTF extrapolation on unmeasured distances becomes a great challenge. Motivated by this, this paper proposes a distance-dependent SH-based model termed DSHM for HRTF representation. DSHM extends the SH-based model by adding a radial part of spherical Fourier-Bessel transform (SFBT). By utilizing a radial correlation between distances, the proposed model has capable of efficient representation for HRTFs over the whole space. As a result, it is feasible to interpolate or extrapolate an HRTF on an unmeasured position. The experimental results show that DSHM achieves a lower LSD when comparing with the conventional SH-based method.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
  3. 3.CAS Center for Excellence in Brain Science and Intelligence TechnologyBeijingChina

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