An individualised head-related transfer function (HRTF) is essential 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 convenient use with a convolutional super-resolution generative adversarial network (SRGAN). This new approach is benchmarked against two baselines: barycentric upsampling and a HRTF selection approach. Experimental results show that the proposed method outperforms both baselines in terms of log-spectral distortion (LSD) and localisation performance using perceptual models when the input HRTF is sparse.

翻译：个性化头部相关传输函数（HRTF）对于构建逼真的虚拟现实（VR）和增强现实（AR）环境至关重要。然而，声学测量高质量的HRTF需要昂贵的设备和消声实验室环境。为了克服这些限制并提高测量效率，过去已有研究利用HRTF上采样技术，通过低分辨率HRTF生成高分辨率HRTF。本文展示了如何将生成对抗网络（GANs）应用于HRTF上采样。我们提出了一种新颖方法，将HRTF数据转换为适用于卷积超分辨率生成对抗网络（SRGAN）的便捷形式。该方法与两种基线方法进行了基准对比：重心上采样法和HRTF选择法。实验结果表明，当输入HRTF稀疏时，所提方法在对数谱失真（LSD）以及基于感知模型的定位性能方面均优于两种基线方法。