## 4 Discussion  
The results presented in Section 3 show that there is no significant difference in the accuracy of the NN when analysing the direct sound of BRIRs captured with both the KEMAR 45 BC and the KU100. However, the accuracy of the NN is significantly reduced when analysing the reflected component of  
Figure 11: Plots of neural network predicted direction of arrival (dotted black line) vs. expected direction of arrival (solid line). The top left plot is for the KEMAR direct sound; the top right plot is for the KU100 direct sound; the bottom left is for the KEMAR reflection; and the bottom right is for the KU100 reflections. Figure 10: Comparison of interaural level difference across the direction of arrival for the KEMAR measured direct sound (**top left**), KEMAR measured reflection (**bottom left**), KU100 measured direct sound (**top right**) and KU100 measured reflection (**bottom right**). the BRIRs, with the NN performing better at predicting the DoA of reflected components measured with the KU100. A reduction in performance would be expected between the direct sound and reflected component, due to the lower signal-to-noise ratio that would be observed for the reflected component. It is of interest that reflections measured with the KU100 are more accurately localised than those measured with the KEMAR 45 BC; this could be due to a greater degree of system misalignment in the KEMAR 45 BC measurements that was not present in the KU100 measurements. An additional difference that could lead to more accurate predictions being made for the KU100 could be the diffuse-field flat frequency response of the KU100, which could produce more consistent spectral cues for the reflected component (as seen in Figure 10), leading to more accurate direction of arrival predictions by the neural network. Analysis over different degrees of measurement orientation rotations (Table 1) showed that while the number of predictions within \(\pm 5^{\circ}\) varies little between degrees of rotation, the maximum error in the neural networks' prediction decreases as the angle of rotation increases. Larger degrees of rotation would produce greater differences in interaural cues between the rotated and original signal, allowing the neural network to produce more accurate predictions under noisier conditions where the interaural cues become blurred. The use of additional measurement orientations decreases the number of front-back confusions, with generally larger degrees of receiver rotations producing fewer front-back hemisphere errors, except when using \(\pm 30^{\circ}\). Using larger degrees of rotation has the additional benefit of reducing the maximum predictions errors made by the neural network; this could be due to the greater rotational mobility allowing signals at the rear of the listener to be focused more in the frontal hemisphere; producing more accurate direction of arrival predictions. It is interesting that there is a greater percentage of front-back confusions for the KEMAR 45 BC compared to the KU100; this could be due to differences in system alignment causing positions close to \(90^{\circ}\) and \(270^{\circ}\) (source facing the left or right ear) to originate from the opposite hemisphere. The lack of significant difference between the direct sounds measured with the two binaural dummy heads agrees with the findings of [PERSON] et al. [11], who found that a GMM trained with an MCT dataset was able to localise sounds captured with two different binaural dummy heads. The notable difference between the KEMAR 45 BC and KU100 include: morphological differences of the head and ears between binaural dummy head microphones; the KEMAR 45 BC has a torso; the KU100's microphones have a flat diffuse-field frequency response; and the material used for the dummy head microphones. The overall accuracy of the method presented in this paper is, however, lower than that found in [11].