## 6 Results
### Follow-up Exploratory Study  
In order to better understand how CNN performs the classification task, a popular gradient-weighted class activation mapping (Grad-CAM) technique [45] was trialed in the pilot tests (the results are not reported in the paper due to space limitations). This method highlights which parts of images constitute important regions in terms of the classification process. However, in our study, the Grad-CAM technique exhibited a relatively poor experimental repeatability, making it difficult to reach meaningful conclusions. Therefore, another visualization method was adopted for our purposes, namely, a modified version of the \"occlusion sensitivity\" technique [46]. In the occlusion sensitivity method, parts of the original input images are obscured, and the influence of such image degradation on the obtained results is quantified. This way, the sensitivity of the model to the occlusion  
Figure 9: Mean classification accuracy scores obtained for human vs. artificial HRTF-independent tests. Sold line illustrates the results for CNN developed using the artificial HRTFs and tested with the human HRTFs. Dashed line signifies the reversed scenario (CNN developed with the human HRTFs and tested with the artificial ones). Error bars represent standard deviations. of certain parts of the images can be ascertained. In the approach proposed in this study, instead of obscuring small parts of the images, it was decided to sequentially occlude selected frequency bands in the spectrograms (one frequency band at a time) and measure the sensitivity of the CNN model to such changes. The obtained results proved to be more repeatable compared to those obtained using the Grad-CAM technique. The modified occlusion sensitivity technique was applied to each excerpt within the binaural repository. Then, the results were averaged. In order to gauge how repeatable the method was, the above procedure was repeated 10 times, and the results were also averaged. The occlusion sensitivity technique was implemented in MATLAB. The results obtained using the above modified occlusion sensitivity technique are presented in Figure 10. They are limited to the narrowest ensembles (width of \(\pm 15^{\circ}\)). The graphs represent the average importance values and the associated 95% confidence intervals calculated across 10 experimental repetitions. The graphs were normalized to unity. An interesting phenomenon can be observed in these figures. Namely, for the ensembles located in the opposite directions (front-back or up-down), the importance curves are complementary to each other for most of the analyzed frequency spectrum (there is a certain degree of horizontal symmetry between the results). For example, for front ensembles, the maximum in the importance curve is seen at approximately 2 kHz, whereas its minimum falls at circa 4.5 kHz (see Figure 10a). A symmetrically \"inverted\" curve was obtained for the back ensembles, with its minimum and maximum being equal to approximately 2 kHz and 4.5 kHz, respectively. A similar complementary effect can also be observed for the up- and down-located ensembles (see Figure 10b). The symmetry effect can only be observed below approximately 7 kHz for front and back ensembles and below 5 kHz for up and down ensembles (it disappears above these frequencies). According to the results obtained for the narrowest ensembles, presented in Figure 10a, the most important frequency region responsible for the localization of front ensembles ranges from approximately 3.5 kHz to 5 kHz, whereas the most important frequency region in terms of the localization of the back ensembles falls with the range between 1.5 kHz and 3 kHz. The results obtained for the up- and down-located ensembles of the same width (\(\pm 15^{\circ}\)) are different (Figure 10b). The most prominent frequency region related to the localization of the up ensembles fits within the range between 5.5 kHz and 8 kHz. The two important frequency bands can be identified in terms of the localization of the down ensembles.