## 6 Results  
### HRTF-Dependent Tests  
According to the results obtained under the HRTF-dependent tests, the average accuracy of the classification of the front-, back-, up-, and down-located music ensembles was 80.26% (standard deviation (SD) 0.68). Further analysis revealed that the classification results depend on the two factors, namely (1) the width of the music ensembles and (2) the HRTF corpora used to synthesize the binaural excerpts, as illustrated in Figure 5. The four graphs presented in the figure correspond to the four HRTF corpora (HUTUBS, SADIE, TH-Koln, and TU-Berlin). Recall that each HRTF corpus contained a triplet of HRTF sets, as shown earlier in Table 1. It can be seen that the best accuracy scores were obtained for the narrowest ensembles (\(\pm 15^{\circ}\)). In this case, the mean classification accuracy scores were relatively high, ranging from 87.6% (for SADIE corpus) up to 91.9% (for TU-Berlin corpus), with an average score of 90.7% (SD 1.0). According to the results depicted in Figure 5, the mean accuracy scores decrease monotonically with the increase in the ensemble width, reaching the lowest values for the ensembles occupying the whole hemispheres (\(\pm 90^{\circ}\)). While in this last-mentioned case, the observed accuracy scores are low, ranging from approximately 60% to 65%, they are still much higher than the no-information rate, which for this experiment was equal to 25% (a classification accuracy value obtained by chance). The deviation from the no-information rate was statistically significant at \(p<0.001\) according to the \(t\)-test. The likely reason for the observed effect of the monotonic decrease in the accuracy scores could be attributed to the less noticeable differences in spectral cues for the sources located near the edges of the \"wide\" ensembles (\(\pm 60^{\circ}\), \(\pm 75^{\circ}\), and \(\pm 90^{\circ}\)). For example, for the up and down ensembles, the differences between the spectrogram components for the sources located directly above and below a listener could be much more pronounced (e.g., due to a head and torso acoustical shadowing), compared to the sources located close to the equator. Another interesting phenomenon that can be seen in Figure 5 is the disparity of the results for the two narrowest music ensembles (\(\pm 15^{\circ}\) and \(\pm 30^{\circ}\)). For these two conditions, the mean accuracy scores obtained for SADIE corpus are approximately five points lower, compared that those obtained for the remaining three corpora (the differences are statistically significant at \(p<0.001\) according to the \(t\)-test). This effect is likely to be explained by the spatial sparsity of SADIE HRTFs near the zenith and nadir, as already illustrated in Figure 3 (recall that the HRTFs were not interpolated in this study). Out of four HRTF corpora used in this study, SADIE was the only one with sparse measurements in the vicinity of the zenith and nadir, potentially introducing some detrimental effects during the training of the model. Figure 6 shows the example confusion matrices obtained for different ensemble widths under the HRTF-dependent test. It can be seen that regardless of the ensemble width, the down-located ensembles are identified the best (with the greatest number of correct classifications in the main diagonal cells). Moreover, it can be observed that CNN struggled to identify the ensembles located above the listener for all the considered ensemble widths. Figure 5: Mean classification accuracy scores obtained under HRTF-dependent tests. Error bars represent standard deviations. Table 3 shows the classification results for the ensemble width of \(\pm 15^{\circ}\). Numbers in brackets represent standard deviations. Table 4 shows the classification results for the ensemble width of \(\pm 90^{\circ}\) (the worst-case scenario). It can be seen that the results are from 24 to 36 percentage points lower, compared to those obtained for the ensembles width of \(\pm 15^{\circ}\).