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A New Selection Method of Anthropometric Parameters in Individualizing HRIR

Journal from gdlhub / 2016-11-17 04:56:45
By : Hugeng Hugeng, Wahidin Wahab, Dadang Gunawan, Telkomnika
Created : 2015-09-01, with 1 files

Keyword : HRIR modeling, HRIR individualization, multiple regression analysis, principal components analysis
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/1792

A trend issue in modeling head-related impulse responses (HRIRs) is how to individualize HRIRs models that are convenient for a particular listener. The objective of this research is to show a robust selection method of eight anthropometric parameters out of all 27 parameters defined in CIPIC HRTF Database. The proposed selection method is systematically and scientifically acceptable, compared to ‘trial and error’ method in selecting the parameters. The selected anthropometric parameters of a given listener were applied in establishing multiple linear regression models in order to individualize his / her HRIRs. We modeled the entire minimum phase HRIRs in horizontal plane of 35 subjects using principal components analysis (PCA). The individual minimum phase HRIRs can be estimated adequately by a linear combination of ten orthonormal basis functions.

Description Alternative :

A trend issue in modeling head-related impulse responses (HRIRs) is how to individualize HRIRs models that are convenient for a particular listener. The objective of this research is to show a robust selection method of eight anthropometric parameters out of all 27 parameters defined in CIPIC HRTF Database. The proposed selection method is systematically and scientifically acceptable, compared to ‘trial and error’ method in selecting the parameters. The selected anthropometric parameters of a given listener were applied in establishing multiple linear regression models in order to individualize his / her HRIRs. We modeled the entire minimum phase HRIRs in horizontal plane of 35 subjects using principal components analysis (PCA). The individual minimum phase HRIRs can be estimated adequately by a linear combination of ten orthonormal basis functions.

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