Individualization of head related transfer functions using principal component analysis

Prior research investigates virtual auditory displays (VADs) using models of HRTFs as a function of a finite number of principal components (PCs) and associated weights (PCWs). This paper studies the effect of PCWs on horizontal plane HRTFs derived from a database of HRIRs and provides a principled approach to PCW tuning. Tuning is first evaluated numerically to determine how variation of PCWs from an average PC model affects HRTF spectral characteristics. An average PC model at 50 azimuths in the horizontal plane is developed from a database of HRIRs of 34 subjects. HRIRs of nine additional subjects are used to test the validity of the average model and to conduct numerical optimization experiments, in which a cost function of spectral distortion is minimized by sequentially tuning PCWs. Sequential tuning mimics how a human would tune a VAD. Numerical results show that sequential tuning of a subset of PCWs reduces spectral distortion metrics when tuning an average HRTF to match an individual HRTF. These experiments show that tuning PCWs can change the shape and frequency location of the pinna notch. The numerical experiments also aid in developing a tuning method that is amenable to human tuning. Several variants of subject tuning experiments are conducted to verify that sequential tuning reduces listening errors. Results of a head steering task show an improvement of 30% in large heading errors when using a tuned VAD relative to an untuned VAD.