基于球面等角投影的生成对抗网络HRTF上采样
HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection
AI总结：
本文提出将HRTF数据变换后输入卷积超分辨率生成对抗网络（SRGAN）进行HRTF上采样，在稀疏测量条件下优于重心、球谐和HRTF选择三种基线方法。
摘要：
个体化头相关传递函数（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，通过声学测量获得高质量HRTF需要昂贵的设备和声学实验室环境。为了克服这些限制并提高测量效率，过去已有研究利用HRTF上采样技术从低分辨率HRTF生成高分辨率HRTF。本文展示了如何将生成对抗网络（GAN）应用于HRTF上采样。我们提出了一种新颖的方法，将HRTF数据变换为可直接用于卷积超分辨率生成对抗网络（SRGAN）的形式。该方法与三个基线方法进行了对比评估：重心上采样、球谐函数（SH）上采样以及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）和基于感知模型的定位性能方面均优于所有三个基线方法。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
