A Frequency-Dependent Head-Related Transfer Functions Modeling Approach Based on Spherical Harmonic Expansion: Frequency-Dependent HRTF Modeling

Yunan Wang, Hongbo Zhao, Wenquan Feng, Dingding Yao

Abstract: Modeling head-related transfer functions (HRTFs) using spherical harmonics (SHs) expansion is an efficient solution for HRTF-related tasks, such as interpolation and binaural rendering. However, the accurate reconstruction of HRTFs requires a large number of SH coefficients. To model HRTFs for accurate perceptual localization performance with fewer SH expansion coefficients, this study proposes a frequency dependent HRTFs modeling approach by utilizing a higher-order SH expansion for the frequency regions that play more important roles for sound localization. The reconstructed HRTFs are then evaluated by the auditory model, which could predict psychoacoustic measures of localization performance. The experimental results show that the proposed method can achieve better HRTF reconstruction for sound source localization with fewer additional SH coefficients, thus can be further used to simplify the complexity of binaural playback for spatial audio applications.