## 4 Performance Evaluation
### Subjective Evaluation  
In this subsection, we carried out a subjective evaluation of the localization experiment in which five listeners (three males and two females) without any auditory disease participated. This experiment was performed using Microsoft Surface Pro 4, which was manufactured by Pegatron Corporation, Taipei, Taiwan, with Sennheiser HD 650 headphones. Table 4 shows the anthropometric measurements of each listener, where male and female listeners are denoted as M1-3 and F1-2, respectively, and \(x_{1}\)-\(x_{17}\) are head and torso measurements, as described in Table 1. Both these anthropometric measurements and each listener's ear images were used as input features for the proposed method. Then, each personalized HRTF was estimated in an environment for each specific listener by the proposed method and the DNN-based method using only head and torso measurements. Next, the estimated HRTFs and average HRTF at a given environment were applied to a speech signal of 10-s duration at a sampling rate of 44.1 kHz. In other words, each HRTF was convoluted with the speech signal. Here, the environments selected in this paper were at eight different azimuths (\(0^{\circ}\), \(\pm 45^{\circ}\), \(\pm 80^{\circ}\), \(\pm 135^{\circ}\), and \(180^{\circ}\)) with an elevation of \(0^{\circ}\). For the evaluation, each participant listened to a pair of speech signals that were convolved with average HRTF, DNN(17) HRTF, or CNN-DNN HRTF, and then he/she judged the azimuth at which each speech signal was assumed to be directed. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Azimuth** & **Average HRTF** & **DNN(37) HRTF** & **DNN(17) HRTF** & **CNN–DNN HRTF** \\ \hline \(-135^{\circ}\) & 11.29 & 4.12 & 6.62 & 5.70 \\ \(-80^{\circ}\) & 4.12 & 3.12 & 4.05 & 3.11 \\ \(-45^{\circ}\) & 7.41 & 3.82 & 5.30 & 4.61 \\ Avg. & 7.61 & 3.69 & 5.32 & 4.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the average log spectral distances (LSDs) for different HRTF estimation methods at \(-135^{\circ}\), \(-80^{\circ}\), and \(-45^{\circ}\). Figure 7: Comparison of the log spectral distances (LSDs) of the individual subject according to different HRTF estimation methods: (**a**) \(-135^{\circ}\), (**b**) \(-80^{\circ}\), and (**c**) \(-45^{\circ}\). Figure 8 illustrates the sound localization performance to show the azimuths judged by the participants versus the target azimuth when average HRTF, DNN(17) HRTF, and CNN-DNN HRTF were used. In the figure, the main diagonal solid line indicates correct judgment, and the upper and lower off-diagonal solid lines indicate \(\pm 30^{\circ}\) margin of error. On the one hand, if a sound is judged on or near the dashed lines, this corresponds to front/back confusion. For example, when the target azimuth was set to \(50^{\circ}\), the sound should be located in the front-right direction. However, the sound seemed to be heard in the back-right direction if the azimuth was judged at \(130^{\circ}\). In addition, two dotted lines parallel to the dashed lines means that the margins of errors are \(\pm 30^{\circ}\). As shown in the figure, judged azimuths when using the proposed method were clustered better than when using average HRTFs or DNN(17) HRTFs. Table 5 compares the accuracies within specific margins, such as \(\pm 15^{\circ}\) and \(\pm 30^{\circ}\), and the front/back confusion rate of the localization experiment between the average HRTF and the proposed method. As shown in the table, the proposed CNN-DNN-based HRTF estimation method achieved higher accuracies of 10% and 12.5% than the average HRTF method for the \(\pm 15^{\circ}\) and \(\pm 30^{\circ}\) margins, respectively. In addition, the accuracy of the proposed method was improved by 10% and 2.5%, compared to the average HRTF method and the DNN(17)-based method, respectively. Moreover, the front/back confusion rate of the proposed method was reduced by 12.5% and 2.5%, compared to the average HRTF method and the DNN(17)-based method, respectively.