This paper deals with the issue of individualizing the head-related transfer function (HRTF) rendering process for auditory elevation perception: is it possible to find a nonindividual, personalized HRTF set that allows a listener to have an equally accurate localization performance than with his/her individual HRTFs? We propose a psychoacoustically motivated, anthropometry based mismatch function between HRTF pairs, that exploits the close relation between the listener’s pinna geometry and localization cues. This is evaluated using an auditory model that computes a mapping between HRTF spectra and perceived spatial locations. Results on a large number of subjects in the CIPIC and ARI HRTF databases suggest that there exists a non-individual HRTF set which allows a listener to have an equally accurate vertical localization than with individual HRTFs. Furthermore, we find the optimal parametrization of the proposed mismatch function, i.e. the one that best reflects the information given by the auditory model. Our findings show that the selection procedure yields statistically significant improvements with respect to dummy-head HRTFs or random HRTF selection, with potentially high impact from an applicative point of view.
Do We Need Individual Head-Related Transfer Functions for Vertical Localization? The Case Study of a Spectral Notch Distance Metric
Simone Spagnol
;
2018-01-01
Abstract
This paper deals with the issue of individualizing the head-related transfer function (HRTF) rendering process for auditory elevation perception: is it possible to find a nonindividual, personalized HRTF set that allows a listener to have an equally accurate localization performance than with his/her individual HRTFs? We propose a psychoacoustically motivated, anthropometry based mismatch function between HRTF pairs, that exploits the close relation between the listener’s pinna geometry and localization cues. This is evaluated using an auditory model that computes a mapping between HRTF spectra and perceived spatial locations. Results on a large number of subjects in the CIPIC and ARI HRTF databases suggest that there exists a non-individual HRTF set which allows a listener to have an equally accurate vertical localization than with individual HRTFs. Furthermore, we find the optimal parametrization of the proposed mismatch function, i.e. the one that best reflects the information given by the auditory model. Our findings show that the selection procedure yields statistically significant improvements with respect to dummy-head HRTFs or random HRTF selection, with potentially high impact from an applicative point of view.File | Dimensione | Formato | |
---|---|---|---|
TASLP_2018.pdf
solo utenti autorizzati
Tipologia:
Versione Editoriale
Licenza:
Accesso ristretto
Dimensione
1.46 MB
Formato
Adobe PDF
|
1.46 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.