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

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Principal components analysis (PCA) is frequently used for modelling the magnitude of the head related 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 al...

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Autores principales: Ramos, Oscar Alberto, Tommasini, Fabián Carlos
Formato: article
Lenguaje:Inglés
Publicado: 2021
Materias:
HRTF
PCA
Binaural audition
Auditory perception
Acceso en línea:http://hdl.handle.net/11086/20831
http://dx.doi.org/10.2478/aoa-2014-0051
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-20831
record_format dspace
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic HRTF
PCA
Binaural audition
Auditory perception
spellingShingle HRTF
PCA
Binaural audition
Auditory perception
Ramos, Oscar Alberto
Tommasini, Fabián Carlos
Magnitude modelling of HRTF using principal component analysis applied to complex values
topic_facet HRTF
PCA
Binaural audition
Auditory perception
description Principal components analysis (PCA) is frequently used for modelling the magnitude of the head related 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◦.
format article
author Ramos, Oscar Alberto
Tommasini, Fabián Carlos
author_facet Ramos, Oscar Alberto
Tommasini, Fabián Carlos
author_sort Ramos, Oscar Alberto
title Magnitude modelling of HRTF using principal component analysis applied to complex values
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
publishDate 2021
url http://hdl.handle.net/11086/20831
http://dx.doi.org/10.2478/aoa-2014-0051
work_keys_str_mv AT ramososcaralberto magnitudemodellingofhrtfusingprincipalcomponentanalysisappliedtocomplexvalues
AT tommasinifabiancarlos magnitudemodellingofhrtfusingprincipalcomponentanalysisappliedtocomplexvalues
bdutipo_str Repositorios
_version_ 1764820392011104258

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