  Head-related transfer functions (HRTFs) are a set of functions describing the
spatial filtering effect of the outer ear (i.e., torso, head, and pinnae) onto
sound sources at different azimuth and elevation angles. They are widely used
in spatial audio rendering. While the azimuth and elevation angles are
intrinsically continuous, measured HRTFs in existing datasets employ different
spatial sampling schemes, making it difficult to model HRTFs across datasets.
In this work, we propose to use neural fields, a differentiable representation
of functions through neural networks, to model HRTFs with arbitrary spatial
sampling schemes. Such representation is unified across datasets with different
spatial sampling schemes. HRTFs for arbitrary azimuth and elevation angles can
be derived from this representation. We further introduce a generative model
named HRTF field to learn the latent space of the HRTF neural fields across
subjects. We demonstrate promising performance on HRTF interpolation and
generation tasks and point out potential future work.
