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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/180599
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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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1844613821776265216 |
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13.070432 |