Towards Faster Continuous Multi-Channel HRTF Measurements Based on Learning System Models

TL;DR

This paper introduces a novel continuous HRTF measurement method that estimates and learns system models offline using Kalman smoothing and expectation maximization, enabling faster measurements with improved accuracy.

Contribution

It presents a new offline estimation approach for continuous HRTF measurement that adapts to faster head rotations, improving measurement quality over traditional methods.

Findings

System distances improved by up to 30 dB.

Effective in simulated single-channel and multi-channel measurements.

Utilizes Kalman smoother and expectation maximization for model learning.

Abstract

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.