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An important problem to be solved in modeling head-related impulse responses(HRIRs) is how to individualize HRIRs so that they are suitable for a listener.We modeled the entire magnitude head-related transfer functions (HRTFs), infrequency domain, for sound sources on horizontal plane of 37 subjects usingprincipal components analysis (PCA). The individual magnitude HRTFs could bemodeled adequately well by a linear combination of only ten orthonormal basisfunctions. The goal of this research was to establish multiple linearregression (MLR) between weights of basis functions obtained from PCA and feweranthropometric measurements in order to individualize a given listener's HRTFswith his or her own anthropomety. We proposed here an improvedindividualization method based on MLR of weights of basis functions byutilizing 8 chosen out of 27 anthropometric measurements. Our objectiveexperiments' results show a superior performance than that of our previous workon individualizing minimum phase HRIRs and also better than similar research.The proposed individualization method shows that the individualized magnitudeHRTFs could approximated well the the original ones with small error. Movingsound employing the reconstructed HRIRs could be perceived as if it was movingaround the horizontal plane.

Improved Method for Individualization of Head-Related Transfer Functions on Horizontal Plane Using Reduced Number of Anthropometric Measurements