HRTF personalization modeling based on RBF neural network

Abstract

A tensor is used to describe head-related transfer functions (HRTFs) dependent on frequencies, sound directions and anthropometric parameters. It can represent the multi-dimensional structure of measured HRTFs. To construct a personalization model, high-order singular value decomposition (HOSVD) is firstly applied to extract individual core tensor as the outputs of the model. Some important anthropometric parameters are selected by Laplacian score and correlation analysis between all measured parameters and the individual core tensor. They act as the inputs of the personalization model. Then a nonlinear model is constructed based on radial basis function (RBF) neural network to predict individual HRTFs according to the measured anthropometric parameters. Compared with back-propagation (BP) neural network method, simulation results demonstrate the better performance for predicting individual HRTFs in the midsaggital plane at high elevations.