  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), in
frequency domain, for sound sources on horizontal plane of 37 subjects using
principal components analysis (PCA). The individual magnitude HRTFs could be
modeled adequately well by a linear combination of only ten orthonormal basis
functions. The goal of this research was to establish multiple linear
regression (MLR) between weights of basis functions obtained from PCA and fewer
anthropometric measurements in order to individualize a given listener's HRTFs
with his or her own anthropomety. We proposed here an improved
individualization method based on MLR of weights of basis functions by
utilizing 8 chosen out of 27 anthropometric measurements. Our objective
experiments' results show a superior performance than that of our previous work
on individualizing minimum phase HRIRs and also better than similar research.
The proposed individualization method shows that the individualized magnitude
HRTFs could approximated well the the original ones with small error. Moving
sound employing the reconstructed HRIRs could be perceived as if it was moving
around the horizontal plane.
