Towards Faster Continuous Multi-Channel HRTF Measurements Based on Learning System Models
This work addresses the need for faster personal HRTF measurements in binaural audio applications, representing an incremental improvement over existing techniques.
The paper tackles the slow rotational velocities in head-related transfer function (HRTF) measurements by introducing a continuous method that uses a Kalman smoother and expectation maximization to learn state-space parameters, achieving up to 30 dB improvement in system distances compared to conventional methods.
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.