  Head-related transfer functions (HRTFs) are crucial for spatial soundfield
reproduction in virtual reality applications. However, obtaining personalized,
high-resolution HRTFs is a time-consuming and costly task. Recently, deep
learning-based methods showed promise in interpolating high-resolution HRTFs
from sparse measurements. Some of these methods treat HRTF interpolation as an
image super-resolution task, which neglects spatial acoustic features. This
paper proposes a spherical convolutional neural network method for HRTF
interpolation. The proposed method realizes the convolution process by
decomposing and reconstructing HRTF through the Spherical Harmonics (SHs). The
SHs, an orthogonal function set defined on a sphere, allow the convolution
layers to effectively capture the spatial features of HRTFs, which are sampled
on a sphere. Simulation results demonstrate the effectiveness of the proposed
method in achieving accurate interpolation from sparse measurements,
outperforming the SH method and learning-based methods.
