An individualised head-related transfer function (HRTF) is very important for creating realistic virtual reality (VR) and augmented reality (AR) environments. However, acoustically measuring high-quality HRTFs requires expensive equipment and an acoustic lab setting. To overcome these limitations and to make this measurement more efficient HRTF upsampling has been exploited in the past where a high-resolution HRTF is created from a low-resolution one. This paper demonstrates how generative adversarial networks (GANs) can be applied to HRTF upsampling. We propose a novel approach that transforms the HRTF data for direct use with a convolutional super-resolution generative adversarial network (SRGAN). This new approach is benchmarked against three baselines: barycentric upsampling, spherical harmonic (SH) upsampling and an HRTF selection approach. Experimental results show that the proposed method outperforms all three baselines in terms of log-spectral distortion (LSD) and localisation performance using perceptual models when the input HRTF is sparse (less than 20 measured positions).

翻译：个体化头部相关传输函数（HRTF）对于创建逼真的虚拟现实（VR）和增强现实（AR）环境至关重要。然而，声学测量高质量HRTF需要昂贵的设备和声学实验室环境。为克服这些限制并提升测量效率，研究者们过去采用了HRTF上采样技术——即从低分辨率HRTF生成高分辨率HRTF。本文展示了生成对抗网络（GAN）如何应用于HRTF上采样。我们提出一种新颖方法，将HRTF数据转换为可直接用于卷积超分辨率生成对抗网络（SRGAN）的形式。该新方法与三种基线方法进行基准对比：重心上采样、球谐函数（SH）上采样和HRTF选择方法。实验结果表明，当输入HRTF稀疏（少于20个测量位置）时，所提方法在对数谱失真（LSD）和基于感知模型的空间定位性能上均优于所有三种基线方法。