Head-related intensity-based transfer function dataset
Head-related intensity-based transfer function dataset
Recently, there has been growth in 3D audio applications in Helds such as virtual reality (VR) and augmented reality (AR). These fields require highly realistic sound to be delivered binaurally to human users. This has generally been achieved by obtaining scalar measurements of sound pressure in or near the human ear, to create a head-related transfer function (HRTF) dataset that characterises the interference of the human anatomy. This paper presents a novel method of improving HRTF datasets by obtaining a head-related intensity-based transfer function (HRIBTF) that characterises the sound at the entrance to the ear canal in terms of intensity, rather than pressure. The vector properties of sound intensity have been proven to inherently provide more useful information in the subjective perception of the sound, which is highly applicable to experiences such as VR and AR. For each ear, the intensity vectors are measured using three micro-electromechanical systems (MEMS) microphone pairs. An analysis of the HRIBTF dataset presented in this paper provides promising conclusions of the applications of sound intensity measurements, in terms of the relationship between the intensity vector and source locations.
head-related transfer function, Intensity, MEMS microphone
1445-1449
European Acoustics Association, EAA
Evans, Kelly
6d351ebc-1fc8-4bea-badc-5a76f14d9c70
Wang, Jiarui
c350a9fd-4ed0-4601-be8e-b3e9768662f7
Abhayapala, Thushara
834037d5-2f67-4d9f-a86c-ffbd6e475f9d
Zhang, Jihui Aimee
6c5536d1-5066-437b-987c-c2307021709d
12 September 2023
Evans, Kelly
6d351ebc-1fc8-4bea-badc-5a76f14d9c70
Wang, Jiarui
c350a9fd-4ed0-4601-be8e-b3e9768662f7
Abhayapala, Thushara
834037d5-2f67-4d9f-a86c-ffbd6e475f9d
Zhang, Jihui Aimee
6c5536d1-5066-437b-987c-c2307021709d
Evans, Kelly, Wang, Jiarui, Abhayapala, Thushara and Zhang, Jihui Aimee
(2023)
Head-related intensity-based transfer function dataset.
Astolfi, Arianna, Asdrubali, Francesco and Shtrepi, Louena
(eds.)
In Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023.
European Acoustics Association, EAA.
.
(doi:10.61782/fa.2023.1051).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Recently, there has been growth in 3D audio applications in Helds such as virtual reality (VR) and augmented reality (AR). These fields require highly realistic sound to be delivered binaurally to human users. This has generally been achieved by obtaining scalar measurements of sound pressure in or near the human ear, to create a head-related transfer function (HRTF) dataset that characterises the interference of the human anatomy. This paper presents a novel method of improving HRTF datasets by obtaining a head-related intensity-based transfer function (HRIBTF) that characterises the sound at the entrance to the ear canal in terms of intensity, rather than pressure. The vector properties of sound intensity have been proven to inherently provide more useful information in the subjective perception of the sound, which is highly applicable to experiences such as VR and AR. For each ear, the intensity vectors are measured using three micro-electromechanical systems (MEMS) microphone pairs. An analysis of the HRIBTF dataset presented in this paper provides promising conclusions of the applications of sound intensity measurements, in terms of the relationship between the intensity vector and source locations.
Text
001051
- Version of Record
More information
Published date: 12 September 2023
Venue - Dates:
10th Convention of the European Acoustics Association, EAA 2023, , Torino, Italy, 2023-09-11 - 2023-09-15
Keywords:
head-related transfer function, Intensity, MEMS microphone
Identifiers
Local EPrints ID: 492899
URI: http://eprints.soton.ac.uk/id/eprint/492899
ISSN: 2221-3767
PURE UUID: 5f9cfca1-92ce-4592-b4ff-eefce3d4d987
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Date deposited: 20 Aug 2024 16:31
Last modified: 22 Aug 2024 02:07
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Contributors
Author:
Kelly Evans
Author:
Jiarui Wang
Author:
Thushara Abhayapala
Author:
Jihui Aimee Zhang
Editor:
Arianna Astolfi
Editor:
Francesco Asdrubali
Editor:
Louena Shtrepi
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