## 7 Discussion  
This work fits within an emerging field of spatial audio scene characterization (SASC). To the best of the authors' knowledge, there are only three similar studies published so far. They are outlined in the first three rows in Table 9. The accuracy levels obtained in this work for the room-dependent (HRTF-dependent) and room-independent (HRTF-independent) test conditions were equal to 89.2% and 74.4%, respectively, as shown in the last row of the table. For consistency of comparison, the above values were calculated for the ensemble width of \(\pm 30^{\circ}\). The outcomes obtained in this work may appear inferior relative to those attained in the third study and superior compared to those achieved in the first two studies shown in the table. However, due to the methodological differences, it is difficult to consistently compare the obtained results against those reported earlier in the literature. The main differences between the studies with the associated implications are discussed below. The first two studies presented in Table 9 were based on the binaural excerpts synthesized using the reverberant room impulse responses (BRIRs), as opposed to the anechoic ones (HRIRs). Reverberant recordings are considered to be more challenging, in terms of the binaural audition modeling, than the anechoic ones [6]. Moreover, the studies differed with regards to the number of the impulse response sets exploited (be they HRIRs or BRIRs). Thirteen BRIR sets were incorporated in the first two studies presented in the table, whereas for the remaining two studies, the numbers of HRIR sets were equal to 74 and 12, respectively. Incorporating a greater number of HRIR sets tends to increase the localization accuracy both for the traditional and deep learning algorithms [22]. Therefore, it could be hypothesized that increasing the number of HRIRs (measured using different heads, microphones, and loudspeakers) could enhance the performance of the model developed in this work. The reason only 12 HRIRs were incorporated in the present study is due to the very limited number of publicly available three-dimensional HRIR sets. Figure 11: Importance of the frequency bands in terms of the discrimination between the ensemble locations based on the CNN sensitivity to obscuring the spectrograms obtained for: (**a**) front and back ensembles, (**b**) up and down ensembles. The graphs were derived for the widest ensembles (\(\pm 90^{\circ}\)). The curves indicate the mean values and associated 95% confidence intervals. The notable differences between the studies include the number of localization categories and the classification methods. The classification accuracy levels cannot be directly compared across the studies if a different number of classification categories (classes) is used. Note that that there were three localization categories (ensemble locations) incorporated in the first two studies, only two categories in the third study, and four categories in the present work (front, back, up, and down). It can be seen in Table 9 that the traditional classification methods were used in the first three studies. They comprised the least absolute shrinkage and selection operator (LASSO), logistic regression (Logit), support vector machines (SVM), and extreme gradient boosting (XGBoost). In addition, CNN was used in the second and the third studies. This work was solely based on CNN as the classification method due to its superior performance proven in the third study. While the first three studies presented in Table 9 were concerned with the localization of the music ensembles solely in a horizontal plane (2D condition), this study is the first one incorporating the ensembles located on a sphere (3D condition). Moreover, for the first three studies, the width of the ensembles was restricted to \(\pm\)30\({}^{\circ}\), whereas in this study, the width (and height) of the ensembles varied between \(\pm\)15\({}^{\circ}\) and \(\pm\)90\({}^{\circ}\).