Spatial upsampling of head-related transfer functions (HRTFs) measured on a sparse grid is an important issue, particularly relevant when capturing individual datasets. While early studies mostly used nearest-neighbor approaches, ongoing research focuses on interpolation in the spherical harmonics (SH) domain. The interpolation can either be performed on the complex spectrum or separately on magnitude and unwrapped phase. Furthermore, preprocessing methods can be applied to reduce the spatial complexity of the HRTF dataset before interpolation. We compare different methods for the interpolation of HRTFs and show that SH and nearest-neighbor based approaches perform comparably. While generally a separate interpolation of magnitude and unwrapped phase outperforms an interpolation of the complex spectra, this can be compensated by appropriate preprocessing methods.
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