  High fidelity spatial audio often performs better when produced using a
personalized head-related transfer function (HRTF). However, the direct
acquisition of HRTFs is cumbersome and requires specialized equipment. Thus,
many personalization methods estimate HRTF features from easily obtained
anthropometric features of the pinna, head, and torso. The first HRTF notch
frequency (N1) is known to be a dominant feature in elevation localization, and
thus a useful feature for HRTF personalization. This paper describes the
prediction of N1 frequency from pinna anthropometry using a neural model.
Prediction is performed separately on three databases, both simulated and
measured, and then by domain mixing in-between the databases. The model
successfully predicts N1 frequency for individual databases and by domain
mixing between some databases. Prediction errors are better or comparable to
those previously reported, showing significant improvement when acquired over a
large database and with a larger output range.
