Abstract
Spherical harmonic (SH)-based methods have been proposed for modeling head-related transfer functions (HRTFs) and yielded an encouraging performance level in terms of log-spectral distortion (LSD). However, most of these techniques model HRTFs on a sphere, and rarely exploit the correlation relationship of HRTFs from different distances, and as a consequence HRTF extrapolation on unmeasured distances becomes a great challenge. Motivated by this, this paper proposes a distance-dependent SH-based model termed DSHM for HRTF representation. DSHM extends the SH-based model by adding a radial part of spherical Fourier-Bessel transform (SFBT). By utilizing a radial correlation between distances, the proposed model has capable of efficient representation for HRTFs over the whole space. As a result, it is feasible to interpolate or extrapolate an HRTF on an unmeasured position. The experimental results show that DSHM achieves a lower LSD when comparing with the conventional SH-based method.
This work is supported by the National Natural Science Foundation of China (NSFC) (No. 61603390), the National Key Research & Development Plan of China (No. 2017YFB1002804), the Major Program for the National Social Science Fund of China (13&ZD189), and the Strategic Priority Research Program of the CAS (Grant XDB02080006).
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Qi, X., Tao, J. (2018). Distance-Dependent Modeling of Head-Related Transfer Functions Based on Spherical Fourier-Bessel Transform. In: Tao, J., Zheng, T., Bao, C., Wang, D., Li, Y. (eds) Man-Machine Speech Communication. NCMMSC 2017. Communications in Computer and Information Science, vol 807. Springer, Singapore. https://doi.org/10.1007/978-981-10-8111-8_13
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DOI: https://doi.org/10.1007/978-981-10-8111-8_13
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