Tuning principal component weights to individualize HRTFs

Prior research has investigated development of virtual auditory displays (VADs) using low-dimensional models of head related transfer functions (HRTFs) as a function of a finite number of principal components (PCs) and associated weights (PCWs). This paper investigates the effect of PCWs on horizontal plane HRTFs derived from a database of HRIRs through analytical optimization experiments. The experiments investigate whether average HRTFs can be tuned to match individual HRTFs. Results provide insight on the effect of tuning PCWs on spectral features of the HRTF. A reduced order modeling technique is used to compactly represent each HRTF. Subject testing results are provided, showing that a human can conduct the tuning procedure and reduce localization errors.