Personalization of Head-Related Transfer Function Based on Sparse Principle Component Analysis and Sparse Representation of 3D Anthropometric Parameters

Abstract

Personalization of head-related transfer functions (HRTFs) is an essential task in the research on virtual hearing. In this paper, a method based on sparse principal component analysis (SPCA) and sparse representation (SR) was proposed to personalize HRTFs. The fundamental assumption is that an equivalent sparse combination can express the same anthropometric parameters. SPCA was first used to reduce the dimensionality of three-dimensional anthropometric parameters, and the reduced physiological parameters were used to reconstruct a physiological parameter database. SR was performed on the reconstructed physiological parameters of all subjects. For each subject, SR was used on the anthropometric parameters that were consistent with those in the reconstruction database. The matching pursuit algorithm was used to obtain the subjects in the database that had the same SR as the subject, and the HRTFs of the matched subject were used as the HRTFs of the new subject. The effect of the proposed method was evaluated by spectral distortion. The results showed that the method did better than others regardless of whether the Chinese pilots’ database or the CIPIC database was used, especially in the 0–8 kHz bandwidth.