Prediction of Head Related Transfer Functions Using Machine Learning Approaches

The generation of a virtual, personal, auditory space to obtain a high-quality sound experience when using headphones is of great significance. Normally this experience is improved using personalized head-related transfer functions (HRTFs) that depend on a large degree of personal anthropometric inf...

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Autores: Fernández Martínez, Roberto, Jimbert Lacha, Pedro José, Sumner, Eric Michael, Riedel, Morris, Unnthorsson, Runar
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/60564
Acceso en línea:http://hdl.handle.net/10810/60564
Access Level:acceso abierto
Palabra clave:head related transfer function
virtual auditory space
artificial neural network
linear regression
modeling methodology
multilayer perceptron
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spelling Prediction of Head Related Transfer Functions Using Machine Learning ApproachesFernández Martínez, RobertoJimbert Lacha, Pedro JoséSumner, Eric MichaelRiedel, MorrisUnnthorsson, Runarhead related transfer functionvirtual auditory spaceartificial neural networklinear regressionmodeling methodologymultilayer perceptronThe generation of a virtual, personal, auditory space to obtain a high-quality sound experience when using headphones is of great significance. Normally this experience is improved using personalized head-related transfer functions (HRTFs) that depend on a large degree of personal anthropometric information on pinnae. Most of the studies focus their personal auditory optimization analysis on the study of amplitude versus frequency on HRTFs, mainly in the search for significant elevation cues of frequency maps. Therefore, knowing the HRTFs of each individual is of considerable help to improve sound quality. The following work proposes a methodology to model HRTFs according to the individual structure of pinnae using multilayer perceptron and linear regression techniques. It is proposed to generate several models that allow knowing HRTFs amplitude for each frequency based on the personal anthropometric data on pinnae, the azimuth angle, and the elevation of the sound source, thus predicting frequency magnitudes. Experiments show that the prediction of new personal HRTF generates low errors, thus this model can be applied to new heads with different pinnae characteristics with high confidence. Improving the results obtained with the standard KEMAR pinna, usually used in cases where there is a lack of information.The authors wish to thank to the Basque Government for its support through the KK-2019-00033 METALCR2, and the University of the Basque Country UPV/EHU for its support through the MOV21/03.MDPI2023202320232023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/60564reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.mdpi.com/2624-599X/5/1/15info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).oai:addi.ehu.eus:10810/605642026-06-18T09:23:17Z
dc.title.none.fl_str_mv Prediction of Head Related Transfer Functions Using Machine Learning Approaches
title Prediction of Head Related Transfer Functions Using Machine Learning Approaches
spellingShingle Prediction of Head Related Transfer Functions Using Machine Learning Approaches
Fernández Martínez, Roberto
head related transfer function
virtual auditory space
artificial neural network
linear regression
modeling methodology
multilayer perceptron
title_short Prediction of Head Related Transfer Functions Using Machine Learning Approaches
title_full Prediction of Head Related Transfer Functions Using Machine Learning Approaches
title_fullStr Prediction of Head Related Transfer Functions Using Machine Learning Approaches
title_full_unstemmed Prediction of Head Related Transfer Functions Using Machine Learning Approaches
title_sort Prediction of Head Related Transfer Functions Using Machine Learning Approaches
dc.creator.none.fl_str_mv Fernández Martínez, Roberto
Jimbert Lacha, Pedro José
Sumner, Eric Michael
Riedel, Morris
Unnthorsson, Runar
author Fernández Martínez, Roberto
author_facet Fernández Martínez, Roberto
Jimbert Lacha, Pedro José
Sumner, Eric Michael
Riedel, Morris
Unnthorsson, Runar
author_role author
author2 Jimbert Lacha, Pedro José
Sumner, Eric Michael
Riedel, Morris
Unnthorsson, Runar
author2_role author
author
author
author
dc.subject.none.fl_str_mv head related transfer function
virtual auditory space
artificial neural network
linear regression
modeling methodology
multilayer perceptron
topic head related transfer function
virtual auditory space
artificial neural network
linear regression
modeling methodology
multilayer perceptron
description The generation of a virtual, personal, auditory space to obtain a high-quality sound experience when using headphones is of great significance. Normally this experience is improved using personalized head-related transfer functions (HRTFs) that depend on a large degree of personal anthropometric information on pinnae. Most of the studies focus their personal auditory optimization analysis on the study of amplitude versus frequency on HRTFs, mainly in the search for significant elevation cues of frequency maps. Therefore, knowing the HRTFs of each individual is of considerable help to improve sound quality. The following work proposes a methodology to model HRTFs according to the individual structure of pinnae using multilayer perceptron and linear regression techniques. It is proposed to generate several models that allow knowing HRTFs amplitude for each frequency based on the personal anthropometric data on pinnae, the azimuth angle, and the elevation of the sound source, thus predicting frequency magnitudes. Experiments show that the prediction of new personal HRTF generates low errors, thus this model can be applied to new heads with different pinnae characteristics with high confidence. Improving the results obtained with the standard KEMAR pinna, usually used in cases where there is a lack of information.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/60564
url http://hdl.handle.net/10810/60564
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2624-599X/5/1/15
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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