  In the growing field of virtual auditory display, personalized head-related
transfer functions (HRTFs) play a vital role in establishing an accurate sound
image. In this work, we propose an HRTF personalization method employing
convolutional neural networks (CNN) to predict a subject's HRTFs for all
directions from their scanned head geometry. To ease the training of the CNN
models, we propose novel pre-processing methods for both the head scans and
HRTF data to achieve compact representations. For the head scan, we use
truncated spherical cap harmonic (SCH) coefficients to represent the pinna
area, which is important in the acoustic scattering process. For the HRTF data,
we use truncated spherical harmonic (SH) coefficients to represent the HRTF
magnitudes and onsets. One CNN model is trained to predict the SH coefficients
of the HRTF magnitudes from the SCH coefficients of the scanned ear geometry
and other anthropometric measurements of the head. The other CNN model is
trained to predict SH coefficients of the HRTF onsets from only the
anthropometric measurements of the ear, head, and torso. Combining the
magnitude and onset predictions, our method is able to predict the complete and
global HRTF data. A leave-one-out validation with the log-spectral distortion
(LSD) metric is used for objective evaluation. The results show a decent LSD
level at both spatial \& temporal dimensions compared to the ground-truth HRTFs
and a lower LSD than the boundary element method (BEM) simulation of HRTFs that
the database provides. The localization simulation results with an auditory
model are also consistent with the objective evaluation metrics, showing the
localization responses with our predicted HRTFs are significantly better than
with the BEM calculated ones.
