HRTF personalization based on artificial neural network in individual virtual auditory space

Abstract

The synthesis of individual virtual auditory space (VAS) is an important and challenging task in virtual reality. One of the key factors for individual VAS is to obtain a set of individual head related transfer functions (HRTFs). A customization method based on back-propagation (BP) artificial neural network (ANN) is proposed to obtain an individual HRTF without complex measurement. The inputs of the neural network are the anthropometric parameters chosen by correlation analysis and the outputs are the characteristic parameters of HRTFs together with the interaural time difference (ITD). Objective simulation experiments and subjective sound localization experiments are implemented to evaluate the performance of the proposed method. Experiments show that the estimated non-individual HRTF has small mean square error, and has similar perception effect to the corresponding one obtained from the database. Furthermore, the localization accuracy of personalized HRTF is increased compared to the non-individual HRTF.