Head-related transfer function (HRTF) is an essential component of a system for creating an immersive listening experience over headphones in multimedia and virtual and augmented reality applications. A critical requirement is the measure of the HRTFs for each individual to accommodate ear idiosyncrasies. Conventional static stop-and-go HRTF measurement methods are tedious and time-consuming. Recently proposed continuous HRTF acquisition methods could improve the acquisition efficiency but they still restrict the movements of the subjects and must be conducted in a controlled environment. In this paper the authors propose a fast and continuous HRTF measurement system with an embedded head tracker that can drastically reduce the measurement duration, obtaining HRTFs at high resolution while still allowing unconstrained head movements in both azimuth and elevation directions. To extract the HRTFs from such dynamic binaural measurements with random head movements, an improved adaptive filtering algorithm is proposed by integrating direction quantization, variable step size, and including optimal HRTF selection into the progressive-based normalized least-mean-squares algorithm. Objective evaluations and subjective listening tests were conducted using measurement data obtained from human subjects. The experimental results demonstrate that the proposed system and algorithm can yield HRTFs that are very close to HRTFs obtained with conventional static methods.
https://www.aes.org/e-lib/browse.cfm?elib=19866
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