A Multilinear Approach to Hrtf Personalization

The head-related transfer function (HRTF) captures most of the auditory cues used to discern sound source direction and therefore is essential for synthesizing convincing spatial audio. HRTFs are, however, difficult to measure and highly person-specific. This has created significant interest in alternative methods for generating custom HRTFs. We present a multilinear modeling framework for HRTFs which makes use of a tensor decomposition called the N -mode SVD [1], a multilinear extension of the conventional singular value decomposition (SVD). Regression is then used to map simple anatomical data to complex HRTF data. This mapping defines a data-driven model capable of producing entire sets of individualized HRTFs from easily obtained anatomical measurements. We show that our approach yields objectively superior results to those of a mapping based on principle components analysis (PCA).