Head-related transfer functions (HRTFs) describe filters that model the scattering effect of the human body on sound waves. In their discrete-time form, they are used in acoustic simulations for virtual reality (VR) or augmented reality (AR), and since HRTFs are listener-specific, the use of individualized HRTFs allows achieving more realistic perceptual results. One way to produce individualized HRTFs is by estimating the sound field around the subjects’ 3D representations (meshes) via numerical simulations, which compute discrete complex pressure values in the frequency domain in regular frequency steps. Despite the advances in the area, the computational resources required for this process are still considerably high and increase with frequency. The goal of this paper is to tackle the high computational cost associated with this task by sampling the frequency domain using hybrid linear-logarithmic frequency resolution. The results attained in simulations performed using 23 real 3D meshes suggest that the proposed strategy is able to reduce the computational cost while still providing remarkably low spectral distortion, even in simulations that require as little as 11.2% of the original total processing time.

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