  Several individualization methods have recently been proposed to estimate a
subject's Head-Related Transfer Function (HRTF) using convenient input
modalities such as anthropometric measurements or pinnae photographs. There
exists a need for adaptively correcting the estimation error committed by such
methods using a few data point samples from the subject's HRTF, acquired using
acoustic measurements or perceptual feedback. To this end, we introduce a
Convolutional Conditional Neural Process meta-learner specialized in HRTF error
interpolation. In particular, the model includes a Spherical Convolutional
Neural Network component to accommodate the spherical geometry of HRTF data. It
also exploits potential symmetries between the HRTF's left and right channels
about the median axis. In this work, we evaluate the proposed model's
performance purely on time-aligned spectrum interpolation grounds under a
simplified setup where a generic population-mean HRTF forms the initial
estimates prior to corrections instead of individualized ones. The trained
model achieves up to 3 dB relative error reduction compared to state-of-the-art
interpolation methods despite being trained using only 85 subjects. This
improvement translates up to nearly a halving of the data point count required
to achieve comparable accuracy, in particular from 50 to 28 points to reach an
average of -20 dB relative error per interpolated feature. Moreover, we show
that the trained model provides well-calibrated uncertainty estimates.
Accordingly, such estimates can inform the sequential decision problem of
acquiring as few correcting HRTF data points as needed to meet a desired level
of HRTF individualization accuracy.
