## 4 Conclusions  
This paper proposes an individualized HRTF customization hybrid algorithm based on ICA decomposition of HRTFs and measurements of anthropometric parameters. The HRTF data of 30 subjects are first decomposed by ICA, and the spatial base vectors as well as weight coefficients are obtained. In parallel, a factor analysis is performed on 27 anthropometric parameters to eliminate redundancy. Then, the weight coefficients from ICA decomposition and anthropometric parameters selected by the factor analysis are fitted by a linear regression analysis to establish an individualized HRTF prediction formula containing anthropometric parameters. According to the objective evaluation of SD, the mean SD values of three of the five subjects in the validation group decrease by 1.2 dB, 1.1 dB, and 1.3 dB, respectively. The subjective localization experiment has also verified that the predicted HRTFs have a better localization effect than the generic KEMAR HRTFs in terms of absolute angle error and front-back and up-down confusion rates.  
This work will facilitate customization of individualized HRTF data quickly and effectively in practical applications of virtual auditory display. It should be noted that, because the spectral characteristics of HRTFs are particularly essential in elevation localization, the proposed customization of individualized HRTF focuses on the elevation directions in the median plane without the presence of binaural localization cues. In principle, the proposed algorithm can be generalized to other spatial directions. Future works can continue to verify the applicability of this algorithm in three-dimensional space.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Elevations** & **Predicted HRTF** & **KEMAR HRTF** & **\(t\)-Test** \\ \hline \multirow{6}{*}{Absolute angle error} & \(-45^{\circ}\) & 10.4\({}^{\circ}\) & 19.6\({}^{\circ}\) & \(t=-1.1\)\(p=0.266\) \\  & 0\({}^{\circ}\) & 11.8\({}^{\circ}\) & 33.3\({}^{\circ}\) & \(t=3.4\)\(p=0.002\) \\ \cline{1-1}  & 22.5\({}^{\circ}\) & 17.2\({}^{\circ}\) & 32\({}^{\circ}\) & \(t=4.3\)\(p=0.000\) \\ \cline{1-1}  & 45\({}^{\circ}\) & 17.2\({}^{\circ}\) & 21.4\({}^{\circ}\) & \(t=2.5\)\(p=0.018\) \\ \cline{1-1}  & 67.5\({}^{\circ}\) & 13.9\({}^{\circ}\) & 19.2\({}^{\circ}\) & \(t=-2.1\)\(p=0.047\) \\ \cline{1-1}  & 90\({}^{\circ}\) & 16.9\({}^{\circ}\) & 33.1\({}^{\circ}\) & \(t=-2.5\)\(p=0.019\) \\ \hline \multicolumn{2}{c}{Front–back confusion rate} & 5.1\% & 10.6\% & \multicolumn{1}{c}{} \\ \multicolumn{2}{c}{Up–down confusion rate} & 6.9\% & 10.2\% & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the localization experiment and corresponding statistical tests.  
**Author Contributions:** [PERSON] contributed to the research idea and framework of this study, and [PERSON] performed the experimental work and data analysis. [PERSON], [PERSON], and [PERSON] discussed and wrote the contents of the manuscript.  
**Funding:** The National Nature Science Foundation of China under Grant No. 11474103 and the Guangdong Modern Vision-audio Information Engineering Technology Research Center (Guangzhou University, 2017.12-2019.12) supported this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.