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Further observations on a principal components analysis of head-related transfer functions

Humans can externalise and localise sound-sources in three-dimensional (3D) space because approaching sound waves interact with the head and external ears, adding auditory cues by (de-)emphasising the level in different frequency bands depending on the direction of arrival. While virtual audio syste...

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Autores principales: Mokhtari, Parham, Kato, Hiroaki, Takemoto, Hironori, Nishimura, Ryouichi, Enomoto, Seigo, Adachi, Seiji, Kitamura, Tatsuya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6522525/
https://www.ncbi.nlm.nih.gov/pubmed/31097764
http://dx.doi.org/10.1038/s41598-019-43967-0
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author Mokhtari, Parham
Kato, Hiroaki
Takemoto, Hironori
Nishimura, Ryouichi
Enomoto, Seigo
Adachi, Seiji
Kitamura, Tatsuya
author_facet Mokhtari, Parham
Kato, Hiroaki
Takemoto, Hironori
Nishimura, Ryouichi
Enomoto, Seigo
Adachi, Seiji
Kitamura, Tatsuya
author_sort Mokhtari, Parham
collection PubMed
description Humans can externalise and localise sound-sources in three-dimensional (3D) space because approaching sound waves interact with the head and external ears, adding auditory cues by (de-)emphasising the level in different frequency bands depending on the direction of arrival. While virtual audio systems reproduce these acoustic filtering effects with signal processing, huge memory-storage capacity would be needed to cater for many listeners because the filters are as unique as the shape of each person’s head and ears. Here we use a combination of physiological imaging and acoustic simulation methods to confirm and extend previous studies that represented these filters by a linear combination of a small number of eigenmodes. Based on previous psychoacoustic results we infer that more than 10, and as many as 24, eigenmodes would be needed in a virtual audio system suitable for many listeners. Furthermore, the frequency profiles of the top five eigenmodes are robust across different populations and experimental methods, and the top three eigenmodes encode familiar 3D spatial contrasts: along the left-right, top-down, and a tilted front-back axis, respectively. These findings have implications for virtual 3D-audio systems, especially those requiring high energy-efficiency and low memory-usage such as on personal mobile devices.
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spelling pubmed-65225252019-05-28 Further observations on a principal components analysis of head-related transfer functions Mokhtari, Parham Kato, Hiroaki Takemoto, Hironori Nishimura, Ryouichi Enomoto, Seigo Adachi, Seiji Kitamura, Tatsuya Sci Rep Article Humans can externalise and localise sound-sources in three-dimensional (3D) space because approaching sound waves interact with the head and external ears, adding auditory cues by (de-)emphasising the level in different frequency bands depending on the direction of arrival. While virtual audio systems reproduce these acoustic filtering effects with signal processing, huge memory-storage capacity would be needed to cater for many listeners because the filters are as unique as the shape of each person’s head and ears. Here we use a combination of physiological imaging and acoustic simulation methods to confirm and extend previous studies that represented these filters by a linear combination of a small number of eigenmodes. Based on previous psychoacoustic results we infer that more than 10, and as many as 24, eigenmodes would be needed in a virtual audio system suitable for many listeners. Furthermore, the frequency profiles of the top five eigenmodes are robust across different populations and experimental methods, and the top three eigenmodes encode familiar 3D spatial contrasts: along the left-right, top-down, and a tilted front-back axis, respectively. These findings have implications for virtual 3D-audio systems, especially those requiring high energy-efficiency and low memory-usage such as on personal mobile devices. Nature Publishing Group UK 2019-05-16 /pmc/articles/PMC6522525/ /pubmed/31097764 http://dx.doi.org/10.1038/s41598-019-43967-0 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Mokhtari, Parham
Kato, Hiroaki
Takemoto, Hironori
Nishimura, Ryouichi
Enomoto, Seigo
Adachi, Seiji
Kitamura, Tatsuya
Further observations on a principal components analysis of head-related transfer functions
title Further observations on a principal components analysis of head-related transfer functions
title_full Further observations on a principal components analysis of head-related transfer functions
title_fullStr Further observations on a principal components analysis of head-related transfer functions
title_full_unstemmed Further observations on a principal components analysis of head-related transfer functions
title_short Further observations on a principal components analysis of head-related transfer functions
title_sort further observations on a principal components analysis of head-related transfer functions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6522525/
https://www.ncbi.nlm.nih.gov/pubmed/31097764
http://dx.doi.org/10.1038/s41598-019-43967-0
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