Modeling individual head-related transfer functions from sparse measurements using a convolutional neural network

Abstract

Individual head-related transfer functions (HRTFs) are usually measured with high spatial resolution or modeled with anthropometric parameters. This study proposed an HRTF individualization method using only spatially sparse measurements using a convolutional neural network (CNN). The HRTFs were represented by two-dimensional images, in which the horizontal and vertical ordinates indicated direction and frequency, respectively. The CNN was trained by using the HRTF images measured at specific sparse directions as input and using the corresponding images with a high spatial resolution as output in a prior HRTF database. The HRTFs of a new subject can be recovered by the trained CNN with the sparsely measured HRTFs. Objective experiments showed that, when using 23 directions to recover individual HRTFs at 1250 directions, the spectral distortion (SD) is around 4.4 dB; when using 105 directions, the SD reduced to around 3.8 dB. Subjective experiments showed that the individualized HRTFs recovered from 105 directions had smaller discrimination proportion than the baseline method and were perceptually undistinguishable in many directions. This method combines the spectral and spatial characteristics of HRTF for individualization, which has potential for improving virtual reality experience.