Modeling and customization of head-related transfer functions using principal component analysis

This study deals with modeling and customization of head-related transfer functions (HRTFs) in the median plane based on a weighted linear summation of a set of general basis functions obtained from a specific huge HRTF database. The 12 principal components (PCs) were extracted from principal components analysis of the median-plane HRTFs in the CIPIC HRTF database. It was verified that the 12 PCs can be general basis functions to model arbitrary subjectpsilas median-plane HRIRs, which are not included in the process to obtain the basis functions, through the quantitative analysis on the modeling error in the least-squares sense and the subjective listening tests. A HRTF customization method based on subjective tuning of the general basis functions was proposed. In the subjective listening test results, all subjects reported better performances for the vertical perception and the front-back discrimination with the customized HRTFs than those with the non-individualized HRTFs except for the front-back confusions of one subject.