Distance-dependent Modeling of Head-related Transfer Functions

Mengfan Zhang; Yue Qiao; Xihong Wu; Tianshu Qu

ICASSP 2019accepted0 citations

Distance-dependent Modeling of Head-related Transfer Functions

Mengfan Zhang, Yue Qiao, Xihong Wu, Tianshu Qu

Abstract

In this paper, a method for modeling distance dependent head-related transfer functions is presented. The HRTFs are first decomposed by spatial principal component analysis. Using deep neural networks, we model the spatial principal component weights of different distances. Then we realize the prediction of HRTFs in arbitrary spatial distances. The objective and subjective experiments are conducted to evaluate the proposed distance model and the distance variation function model, and the results have shown that the proposed model has less spectral distortions than distance variation function model, and the virtual sound generated by the proposed model has better performance in terms of distance localization.

BibTeX

@inproceedings{icassp2019_distancedependen,
  title = {Distance-dependent Modeling of Head-related Transfer Functions},
  author = {Mengfan Zhang and Yue Qiao and Xihong Wu and Tianshu Qu},
  booktitle = {ICASSP 2019},
  year = {2019}
}