Efficient Continuous HRTF Model Using Data Independent Basis Functions: Experimentally Guided Approach

• Published in: IEEE transactions on audio, speech, and language processing Vol. 17; no. 4; pp. 819 - 829
• Main Authors: Wen Zhang, Kennedy, R.A., Abhayapala, T.D.
• Format: Journal Article
• Language: English
• Published: Piscataway, NJ IEEE 01.05.2009; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Approximation; Basis functions; Continuous model; Discrete Fourier transforms; Exact sciences and technology; Filter bank; Fourier series; Fourier-Bessel series; Frequency measurement; head-related transfer function (HRTF); Humans; Information, signal and communications theory; Interpolation; Layout; Mathematical models; Natural language processing; Optimization; Principal component analysis; Series expansion; Shape; Signal and communications theory; Signal representation. Spectral analysis; Signal, noise; Spectra; Telecommunications and information theory; Transfer functions; Performance evaluation; Bessel function; Karhunen Loeve transformation; head-related transfer function (HRTF); Continuous model; Fourier series; Modeling; Fourier-Bessel series; Statistical method; Time domain method; Sound analysis; Series expansion; Transfer function; Principal component analysis
• ISSN: 1558-7916
• DOI: 10.1109/TASL.2009.2014265

This paper introduces a continuous functional model for head-related transfer functions (HRTFs) in the horizontal auditory scene. The approach uses a separable representation consisting of a Fourier-Bessel series expansion for the spectral components and a conventional Fourier series expansion for the spatial components. Being independent of the data, these two sets of basis functions remain unchanged for all subjects and measurement setups. Hence, the model can transform an individualized HRTF to a subject specific set of coefficients. A continuous functional model is also developed in the time domain. We show the efficient model performance in approximating experimental measurements by using the HRTF measurements from a KEMAR manikin and the synthetic data from the spherical head model. The statistical results are determined from a 50-subject HRTF data set. We also corroborate the predictive capability of the proposed model. The model has near optimal performance, which can be ascertained by comparison with the standard principle component analysis (PCA) and discrete Karhunen-Loeve expansion (KLE) methods at the measurement points and for a given number of parameters.