Head-related transfer function (HRTF) is essential to realize an immersive listening experience over headphones, which is unique for every individual. Conventionally, HRTFs are measured using discrete stop-and-go method for multiple loudspeaker positions, which is a tedious and time consuming process, especially for human subjects. Recently, continuous HRTF acquisition methods have been proposed to improve the acquisition efficiency. However, these methods still require constrained or limited movements of subjects and can only be used in a controlled environment. In this paper, we present a novel fast and continuous HRTF acquisition system that incorporates head-tracker to allow unconstrained head movements in azimuth and elevation. An improved adaptive filtering approach that combines conventional progressive based normalized least mean square algorithm (NLMS) and previously proposed activated based NLMS is proposed to extract HRTFs on-the-fly from such binaural measurements with random head movements in both azimuth and elevation. Experimental results demonstrate that the proposed approach significantly enhances the performance of conventional progressive NLMS for short duration measurements and further validates the accuracy of proposed HRTF acquisition method.
https://www.aes.org/e-lib/browse.cfm?elib=18351
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