  Measuring personal head-related transfer functions (HRTFs) is essential in
binaural audio. Personal HRTFs are not only required for binaural rendering and
for loudspeaker-based binaural reproduction using crosstalk cancellation, but
they also serve as a basis for data-driven HRTF individualization techniques
and psychoacoustic experiments. Although many attempts have been made to
expedite HRTF measurements, the rotational velocities in today's measurement
systems remain lower than those in natural head movements. To cope with faster
rotations, we present a novel continuous HRTF measurement method. This method
estimates the HRTFs offline using a Kalman smoother and learns state-space
parameters, including the system model, on short signal segments, utilizing the
expectation maximization algorithm. We evaluated our method in simulated
single-channel and multi-channel measurements using a rigid sphere HRTF model.
Comparing with conventional methods, we found that the system distances are
improved by up to 30 dB.
