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.

翻译：头部相关传递函数（HRTFs）对于虚拟现实应用中的空间声场再现至关重要。然而，获取个性化、高分辨率的HRTFs是一项耗时且成本高昂的任务。近年来，基于深度学习的方法在从稀疏测量数据中插值高分辨率HRTFs方面展现出潜力。其中部分方法将HRTF插值视为图像超分辨率任务，这忽略了空间声学特征。本文提出一种基于球面卷积神经网络（Spherical CNN）的HRTF插值方法。该方法通过球谐函数（SHs）对HRTF进行分解与重构来实现卷积过程。SHs是定义在球面上的正交函数集，使得卷积层能够有效捕捉采样于球面上的HRTF的空间特征。仿真结果表明，该方法能够从稀疏测量数据中实现精确插值，性能优于传统的SH方法和基于学习的方法。