## 6 Results
### HRTF-Independent Tests
In contrast to Figure 5, which presents the results for the single CNN model tested under the HRTF-dependent conditions, Figure 7 displays the results for four CNN models developed separately for each data fold listed in Table 5. The curves presented in the figure were annotated according to the corpus used for testing. For example, the results labeled as HUTUBS represent the accuracy scores obtained for the model which was developed using SADIE, TH-Koln, and TU-Berlin HRTF corpora and then tested on the excerpts synthesized using solely HUTUBS corpus (fold No. 1). The main observation that can be made when inspecting the accuracy scores in Figure 7 is that they are approximately 10 to 20 percentage points lower compared to those presented earlier in Figure 5. Similar to the trend observed formerly under the HRTF-dependent test scenario, the accuracy scores also tend to decrease with the increase in the ensemble width. However, this effect is only noticeable for the relatively wide ensembles (\(\pm 60^{\circ}\), \(\pm 75^{\circ}\), and \(\pm 90^{\circ}\)). For the widest ensembles (\(\pm 90^{\circ}\)), the observed accuracy levels are relatively low, being equal to 55.5% for SADIE and approximately 47% for the remaining three HRTF corpora. Nevertheless, these classification levels considerably exceed the no-information rate (25%). The differences from the no-information rate are statistically significant at \(p<0.001\) according to the \(t\)-test. Note that for the ensemble widths of \(\pm 60^{\circ}\) and \(\pm 75^{\circ}\), the results obtained for the TH-Koln corpus were lower compared to those obtained with the remaining three corpora (\(p<0.001\) according to the \(t\)-test). This indicates that the model \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fold No.** & **HRTF Corpora Used for Development** & **HRTF Corpus Used for Testing** \\ \hline
1 & SADIE, TH-Koln, TU-Berlin & HUTUBS \\
2 & HUTUBS, TH-Koln, TU-Berlin & SADIE \\
3 & SADIE, HUTUBS, TU-Berlin & TH-Koln \\
4 & SADIE, HUTUBS, TH-Koln & TU-Berlin \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data folds used for HRTF-independent tests. trained with the binaural excerpts synthesized using HRTFs derived from \"head and torso\" do not always generalize well when applied for the excerpts convolved with the HRTFs measured solely with the head (without torso), which was the case for the TH-Koln corpus (in this instance, the Neumann KU 100 artificial head was used). Figure 8 shows the selected examples of the confusion matrices obtained under the HRTF-independent tests. The top three matrices illustrate the results obtained for the ensemble width of \(\pm\)60\({}^{\circ}\), whereas the remaining three matrices characterize the results achieved for the ensemble width of \(\pm\)90\({}^{\circ}\) (the worst-case scenario). While for the SADIE corpus (Figure 8a) the classification results could be considered relatively good, for the remaining conditions, CNN exhibited a substantial number of misclassifications, particularly for the widest ensemble width (\(\pm\)90\({}^{\circ}\)). Table 6 shows the results obtained for SADIE corpus for the ensemble width of \(\pm\)60\({}^{\circ}\) expressed in terms of the precision, recall, and \(F1\)-score. It can be seen that the classification results are relatively satisfactory, with an \(F1\)-score ranging from 61.5% (for up-located ensembles) to 74% (for down-located ensembles). By contrast, the results obtained for the TH-Koln corpus for the same ensemble width are worse (see Table 7), particularly for the up-located ensembles. For the aforementioned condition, recall was equal to only 22.9%, with an \(F1\)-score amounting to 35.5%.