Reconstruction of individualized near-field head-related transfer functions from a small set of far-field data based on tensor decomposition

Abstract

Head-related transfer functions (HRTFs) are essential for virtual auditory display. Generally, near-field HRTFs vary with source direction, distance, frequency, and individual. The huge dimensionality of data makes the measurement or calculation of individualized near-field HRTFs difficult. In the present work, a method to estimate individualized near-field HRTFs with high directional and distance resolution from a small set of directional measured or calculated data at a far-field distance is proposed. Based on tensor decomposition, the near-field HRTFs are decomposed into a weighted combination of direction-, distance-, frequency-, and a few of individual-related modes. The universal direction-, distance-, and frequency-modes as well as the weights are evaluated from a baseline near-field HRTFs dataset. For an arbitrary new individual outside the baseline dataset, the individual modes are estimated from a small set of directional measured or calculated of far-field HRTFs, and then the individualized near-field HRTFs with high directional resolution are estimated. An example of analysis indicates that 15 individual modes account for more than 98 % individual-related energy variation of HRTFs; and near-field HRTFs with high directional resolution can be estimated from far-field data of 30 directional measurements or calculations. Two psychoacoustic experiments validate the proposed method.