Interpolation method of head-related transfer functions based on common-pole/zero modeling

The head-related transfer function (HRTF) involves the cues for human auditory localization, which turns it into an essential item of virtual auditory display technology. In practice, the interpolation of HRTF is necessary for the virtual auditory display systems to achieve high spatial resolution. Traditional geometric-based interpolation methods are generally restrained by the spatial distribution of reference on HRTF. When the spatial distribution is sparse, the accuracy of interpolation decreases significantly. Therefore, an interpolation method using the common-pole/zero model and the fitting neural network is proposed. First, we propose a common-pole/zero model to represent HRTFs across multiple subjects, in which the low-dimensional features of the measured HRTFs are extracted. Then, for a new spatial direction, we predict the corresponding low-dimensional HRTF with a fitting neural network. Finally, we reconstruct the high-dimensional HRTF from the predicted low-dimensional HRTF. The simulation results suggest that the proposed method outperforms other interpolation methods such as Linear_AMBC, Bilinear_AMBC, and the Combination method.