Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169107
Title: Implementing continuous HRTF measurement in near-field
Authors: Tan, Ee-Leng
Peksi, Santi
Gan, Woon-Seng
Keywords: Science::Physics::Acoustics
Engineering::Electrical and electronic engineering
Issue Date: 2023
Source: Tan, E., Peksi, S. & Gan, W. (2023). Implementing continuous HRTF measurement in near-field. 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023). https://dx.doi.org/10.1109/ICASSP49357.2023.10096536
Project: MOE-T2EP20221-0014 
Conference: 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023)
Abstract: Head-related transfer function (HRTF) is an essential component to create an immersive listening experience over headphones for virtual reality (VR) and augmented reality (AR) applications. Metaverse combines VR and AR to create immersive digital experiences, and users are very likely to interact with virtual objects in the near-field (NF). The HRTFs of such objects are highly individualized and dependent on directions and distances. Hence, a significant number of HRTF measurements at different distances in the NF would be needed. Using conventional static stop-and-go HRTF measurement methods to acquire these measurements would be time-consuming and tedious for human listeners. In this paper, we propose a continuous measurement system targeted for the NF, and efficiently capturing HRTFs in the horizontal plane within 45 secs. Comparative experiments are performed on head and torso similar (HATS) and human listeners to evaluate system consistency and robustness.
URI: https://hdl.handle.net/10356/169107
ISBN: 978-1-7281-6327-7
DOI: 10.1109/ICASSP49357.2023.10096536
Schools: School of Electrical and Electronic Engineering 
Rights: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICASSP49357.2023.10096536.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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