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Applied Methods for Sparse Sampling of Head-related Transfer Functions

小编 映维   |   分类：Perception / XR   |  

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PubDate: June 7, 2021

Teams: Ben-Gurion University of the Negev; Facebook Reality Labs Research

Writers: Lior Arbel, Zamir Ben-Hur, David Lou Alon, Boaz Rafaely

PDF: Applied Methods for Sparse Sampling of Head-related Transfer Functions

Abstract

Production of high fidelity spatial audio applications requires individual head-related transfer functions (HRTFs). As the acquisition of HRTF is an elaborate process, interest lies in interpolating full length HRTF from sparse samples. Ear-alignment is a recently developed pre-processing technique, shown to reduce an HRTF’s spherical harmonics order, thus permitting sparse sampling over fewer directions. This paper describes the application of two methods for ear-aligned HRTF interpolation by sparse sampling: Orthogonal Matching Pursuit and Principal Component Analysis. These methods consist of generating unique vector sets for HRTF representation. The methods were tested over an HRTF dataset, indicating that interpolation errors using small sampling schemes may be further reduced by up to 5 dB in comparison with spherical harmonics interpolation.

本文链接：https://paper.nweon.com/10262

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