Abstract
A new customization approach based on support vector regression (SVR) is proposed to obtain individual head-related impulse response (HRIR) without complex measurement and special equipment. Principal component analysis (PCA) is first applied to obtain a few principal components and corresponding weight vectors correlated with individual anthropometric parameters. Then the weight vectors act as output of the nonlinear regression model. Some measured anthropometric parameters are selected as input of the model according to the correlation coefficients between the parameters and the weight vectors. After the regression model is learned from the training data, the individual HRIR can be predicted based on the measured anthropometric parameters. Compared with a back-propagation neural network (BPNN) for nonlinear regression, better generalization and prediction performance for small training samples can be obtained using the proposed PCA-SVR algorithm.
This is a preview of subscription content, log in via an institution to check access.
References
Otami M, Ise S. Fast calculation system specialized for head-related transfer function [J]. Journal of the Acoustical Society of America, 2006, 119(5): 2589–2598.
Kahana Y, Nelson P A. Boundary element simulations of the transfer function of human heads and baffled pinnae using accurate geometric models [J]. Journal of Sound and Vibration, 2007, 119(5): 552–579.
Xiao T, Liu Q H. Finite difference computation of head-related transfer function for human hearing [J]. Journal of the Acoustical Society of America, 2003, 113(5): 2434–2441.
Mokhtari P, Takemoto H, Nishimura R, Kato H. Computer simulation of HRTFs for personalization of 3D audio [C]// The Second International Symposium on Universal Communication, Venue, Osaka. 2008: 435–440.
Hu H, Chen L, Wu Z Y. The estimation of personalized HRTFs in individual VAS [C]// The Fourth International Conference on Natural Computation, Jinan. 2008: 203–207.
Hu H, Zhou L, Ma H, Wu Z. HRTF personalization based on artificial neural network in individual virtual auditory space [J]. Applied Acoustics, 2008, 69(2): 163–172.
Wall M E, Andreas R, Luis M R. Singular value decomposition and principal component analysis [M]// A practical approach to microarray data analysis, Kluwer: Norwell, 2003: 91–109.
Pearson K. Mathematical contributions to the theory of evolution, III: Regression, heredity and panmixia [J]. Philosophical Transactions of the Royal Society of London A, 1896, 187: 253–318.
Lu C J, Lee T S, Chiu C C. Financial time series forecasting using independent component analysis and support vector regression [J]. Decision Support Systems, 2009, 47(2): 115–125.
Smola A J, Scholkopf B. A tutorial on support vector regression [J]. Statistics and Computing, 2004, 14(3): 199–222.
Algazi V R, Dudo R O, Thompson D M, Avendano C. The CIPIC HRTF database [C]//IEEE Workshop on the Application of Signal Processing to Audio and Acoustics, New Platz, New York. 2001: 99–102.
CIPIC HRIR database, Release 1.2 [CP/OL]. (2003-9) [2006-9-6]. http://interface.cipic.ucdavis.edu/.
Chang C C, Lin C J. LIBSVM: A library for support vector machines [CP/OL]. (2006-2) [2006-12-11]. http://www.csie.ntu.edu.tw/cjlin/libsvm.
Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression [J]. Neural Network, 2004, 17(1): 113–126.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by WANG Shuo-zhong
Project supported by the Shanghai Natural Science Foundation (Grant No.08ZR1408300), and the Shanghai Leading Academic Discipline Project (Grant No.S30108)
About this article
Cite this article
Huang, Qh., Fang, Y. Modeling personalized head-related impulse response using support vector regression. J. Shanghai Univ.(Engl. Ed.) 13, 428–432 (2009). https://doi.org/10.1007/s11741-009-0602-2
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11741-009-0602-2