Magnitude modelling of HRTF using principal component analysis applied to complex values

Autores
Ramos, Oscar Alberto; Tommasini, Fabián Carlos
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Principal components analysis (PCA) is frequently used for modelling the magnitude of the headrelated transfer functions (HRTFs). Assuming that the HRTFs are minimum phase systems, the phase is obtained from the Hilbert transform of the log-magnitude. In recent years, the PCA applied to HRTFs is also used to model individual HRTFs relating the PCA weights with anthropometric measurements of the head, torso and pinnae. The HRTF log-magnitude is the most used format of input data to the PCA, but it has been shown that if the input data is HRTF linear magnitude, the cumulative variance converges faster, and the mean square error (MSE) is smaller. This study demonstrates that PCA applied directly on HRTF complex values is even better than the two formats mentioned above, that is, the MSE is the smallest and the cumulative variance converges faster after the 8th principal component. Different objective experiments around all the median plane put in evidence the differences which, although small, seem to be perceptually detectable. To elucidate this point, psychoacoustic discrimination tests are done between measured and reconstructed HRTFs from the three types of input data mentioned, in the median plane between −45◦ and +90◦ .
Fil: Ramos, Oscar Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centro de Investigación y Transferencia en Acústica; Argentina
Fil: Tommasini, Fabián Carlos. Centro de Investigación y Transferencia en Acústica; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
Materia
HRTF
PCA
BINAURAL AUDITION
AUDITORY AUDITION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/180599

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spelling Magnitude modelling of HRTF using principal component analysis applied to complex valuesRamos, Oscar AlbertoTommasini, Fabián CarlosHRTFPCABINAURAL AUDITIONAUDITORY AUDITIONhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1https://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Principal components analysis (PCA) is frequently used for modelling the magnitude of the headrelated transfer functions (HRTFs). Assuming that the HRTFs are minimum phase systems, the phase is obtained from the Hilbert transform of the log-magnitude. In recent years, the PCA applied to HRTFs is also used to model individual HRTFs relating the PCA weights with anthropometric measurements of the head, torso and pinnae. The HRTF log-magnitude is the most used format of input data to the PCA, but it has been shown that if the input data is HRTF linear magnitude, the cumulative variance converges faster, and the mean square error (MSE) is smaller. This study demonstrates that PCA applied directly on HRTF complex values is even better than the two formats mentioned above, that is, the MSE is the smallest and the cumulative variance converges faster after the 8th principal component. Different objective experiments around all the median plane put in evidence the differences which, although small, seem to be perceptually detectable. To elucidate this point, psychoacoustic discrimination tests are done between measured and reconstructed HRTFs from the three types of input data mentioned, in the median plane between −45◦ and +90◦ .Fil: Ramos, Oscar Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centro de Investigación y Transferencia en Acústica; ArgentinaFil: Tommasini, Fabián Carlos. Centro de Investigación y Transferencia en Acústica; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaPolish Acad Sciences Inst Fundamental Technological Research2014-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/180599Ramos, Oscar Alberto; Tommasini, Fabián Carlos; Magnitude modelling of HRTF using principal component analysis applied to complex values; Polish Acad Sciences Inst Fundamental Technological Research; Archives Of Acoustics; 39; 4; 12-2014; 477-4820137-5075CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.2478/aoa-2014-0051info:eu-repo/semantics/altIdentifier/url/https://journals.pan.pl/aoa/116743info:eu-repo/semantics/altIdentifier/url/https://acoustics.ippt.pan.pl/index.php/aa/article/view/294info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:02:07Zoai:ri.conicet.gov.ar:11336/180599instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:02:07.679CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Magnitude modelling of HRTF using principal component analysis applied to complex values
title Magnitude modelling of HRTF using principal component analysis applied to complex values
spellingShingle Magnitude modelling of HRTF using principal component analysis applied to complex values
Ramos, Oscar Alberto
HRTF
PCA
BINAURAL AUDITION
AUDITORY AUDITION
title_short Magnitude modelling of HRTF using principal component analysis applied to complex values
title_full Magnitude modelling of HRTF using principal component analysis applied to complex values
title_fullStr Magnitude modelling of HRTF using principal component analysis applied to complex values
title_full_unstemmed Magnitude modelling of HRTF using principal component analysis applied to complex values
title_sort Magnitude modelling of HRTF using principal component analysis applied to complex values
dc.creator.none.fl_str_mv Ramos, Oscar Alberto
Tommasini, Fabián Carlos
author Ramos, Oscar Alberto
author_facet Ramos, Oscar Alberto
Tommasini, Fabián Carlos
author_role author
author2 Tommasini, Fabián Carlos
author2_role author
dc.subject.none.fl_str_mv HRTF
PCA
BINAURAL AUDITION
AUDITORY AUDITION
topic HRTF
PCA
BINAURAL AUDITION
AUDITORY AUDITION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Principal components analysis (PCA) is frequently used for modelling the magnitude of the headrelated transfer functions (HRTFs). Assuming that the HRTFs are minimum phase systems, the phase is obtained from the Hilbert transform of the log-magnitude. In recent years, the PCA applied to HRTFs is also used to model individual HRTFs relating the PCA weights with anthropometric measurements of the head, torso and pinnae. The HRTF log-magnitude is the most used format of input data to the PCA, but it has been shown that if the input data is HRTF linear magnitude, the cumulative variance converges faster, and the mean square error (MSE) is smaller. This study demonstrates that PCA applied directly on HRTF complex values is even better than the two formats mentioned above, that is, the MSE is the smallest and the cumulative variance converges faster after the 8th principal component. Different objective experiments around all the median plane put in evidence the differences which, although small, seem to be perceptually detectable. To elucidate this point, psychoacoustic discrimination tests are done between measured and reconstructed HRTFs from the three types of input data mentioned, in the median plane between −45◦ and +90◦ .
Fil: Ramos, Oscar Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centro de Investigación y Transferencia en Acústica; Argentina
Fil: Tommasini, Fabián Carlos. Centro de Investigación y Transferencia en Acústica; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina
description Principal components analysis (PCA) is frequently used for modelling the magnitude of the headrelated transfer functions (HRTFs). Assuming that the HRTFs are minimum phase systems, the phase is obtained from the Hilbert transform of the log-magnitude. In recent years, the PCA applied to HRTFs is also used to model individual HRTFs relating the PCA weights with anthropometric measurements of the head, torso and pinnae. The HRTF log-magnitude is the most used format of input data to the PCA, but it has been shown that if the input data is HRTF linear magnitude, the cumulative variance converges faster, and the mean square error (MSE) is smaller. This study demonstrates that PCA applied directly on HRTF complex values is even better than the two formats mentioned above, that is, the MSE is the smallest and the cumulative variance converges faster after the 8th principal component. Different objective experiments around all the median plane put in evidence the differences which, although small, seem to be perceptually detectable. To elucidate this point, psychoacoustic discrimination tests are done between measured and reconstructed HRTFs from the three types of input data mentioned, in the median plane between −45◦ and +90◦ .
publishDate 2014
dc.date.none.fl_str_mv 2014-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/180599
Ramos, Oscar Alberto; Tommasini, Fabián Carlos; Magnitude modelling of HRTF using principal component analysis applied to complex values; Polish Acad Sciences Inst Fundamental Technological Research; Archives Of Acoustics; 39; 4; 12-2014; 477-482
0137-5075
CONICET Digital
CONICET
url http://hdl.handle.net/11336/180599
identifier_str_mv Ramos, Oscar Alberto; Tommasini, Fabián Carlos; Magnitude modelling of HRTF using principal component analysis applied to complex values; Polish Acad Sciences Inst Fundamental Technological Research; Archives Of Acoustics; 39; 4; 12-2014; 477-482
0137-5075
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.2478/aoa-2014-0051
info:eu-repo/semantics/altIdentifier/url/https://journals.pan.pl/aoa/116743
info:eu-repo/semantics/altIdentifier/url/https://acoustics.ippt.pan.pl/index.php/aa/article/view/294
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Polish Acad Sciences Inst Fundamental Technological Research
publisher.none.fl_str_mv Polish Acad Sciences Inst Fundamental Technological Research
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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