A Splicing Interpolation Method for Head-Related Transfer Function

Abstract

We proposed a new head-related transfer function (HRTF) interpolation method based on splicing. The sound spreads from sound source to listener’s ears, the wave of head-related impulse response (HRIR) clearly indicates time delay which can be symbolized by the sample number of the first obvious peak and the signal filtered by the diffraction and reflection properties of the torso, head, and pinna. Based on this feature and the importance of the time delay in interpolation, we split the measured HRIR into three parts according to the first obvious peak of the wave and the length of the useful signal. First, we used radial basis function neural network to construct regression forecast models using spatial parameters (i.e., azimuth, elevation, and distance) and the HRIRs sample number of the first obvious peak. Then, we used the tetrahedral interpolation with barycentric weights to calculate the second part of the HRIR. Finally, we spliced the forecast information and the useful signal in terms of the sample number of the first obvious peak. For an unknown position in three-dimensional space of near-field, we interpolated its HRIR by the proposed method. The Signal-to-Deviation ratio (SDR) increased 5 dB compared with the tetrahedron interpolation in the time domain.