A Virtual Listener for HRTF-Based Sound Source Localization Using Support Vector Regression

In perceptual-based techniques for individualization of head-related transfer functions (HRTFs), subjects tune some parameters for several target directions until they achieve an acceptable spatial accuracy. However, this procedure might be time-consuming depending on the ability of the listener, and the number of parameters and target directions. This makes desirable a way to estimate empirically the localization accuracy before tuning sessions. To tackle this problem, we propose a virtual listener based on Support Vector Regression (SVR) to substitute the human listener in such sessions. We show that, using a small training set obtained by sampling uniformly a subject's HRTFs across directions, our virtual listener achieves human-level localization accuracy. Moreover, simulations show that the virtual listener performance is in accordance with human perception for sound sources with different frequency content as well as sound sources filtered through non-individualized HRTFs. Finally, our approach based on SVR attains performance similar to computationally intensive methods based on Gaussian Process Regression.