  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).
