## 3 Results  
The two NNs trained with the SADIE HRIR dataset (as described in Sections 2.1 and 2.2) were tested with the components of the measured test BRIRs (as described in Section 2.4), with the outputs concatenated to produce the resulting direction of arrival for the direct and reflected components. The angular error was then computed as the difference between the NN predictions and the target values. The training of the neural network generally terminated due to saturation in output performance within 122 epochs, with an accuracy of 95% and a maximum error of 5\({}^{\circ}\). Statistical analysis of the prediction errors was performed using MATLAB's one-way analysis of variance (ANOVA) function [36] and is reported in the format: ANOVA(\(F\)(between group degrees of freedom, within groups degree of freedom) = \(F\) value, \(p\) = significance), all of these values are returned by the anova1 function [36]. A baseline method used as a reference to compare results obtained from the NN can be derived from the ITD equation (Equation (8) taken from [37]) rearranged for calculating the DoA,  
\[ITD=\frac{d\sin(\theta_{ref})}{c} \tag{8}\]  
where \(d\) is the distance between the two ears, \(\theta_{ref}\) is the DoA, and \(c\) is the speed of sound [37]. The \(ITD\) value used for the baseline DoA predictions was measured by locating the maximum peak in the \(IACC\)  
Figure 5: Example binaural room impulse response generated with source at azimuth = 0\({}^{\circ}\) and reflector at azimuth = 71\({}^{\circ}\); the solid line is the left channel of the impulse response; the dotted line is the right channel of the impulse response; and the windowed area denotes the segmented regions using the technique discussed in Section 2.4.  
feature vector, as calculated using the binaural model proposed in Section 2.1. The index for this peak in the _IACC_ feature vector relates to one of 99 _ITD_ values linearly spaced from \(-1.1\) ms to \(1.1\) ms. In Table 3, the neural network accuracy across the test data is presented. The results show that for the direct sound, the neural network predicted 64.58% and 68.06% of the DoAs within 5\({}^{\circ}\) for the KEMAR and KU100 dummy head, respectively. Although when analysing direct sound captured with the KU100, a greater percentage of predictions are within \(\pm 5^{\circ}\) of the target value, the neural network makes a greater number of exact predictions and has lower relative error for KEMAR. This observation is expected given the different morpho-acoustic properties of each head and their ears, which could lead to differences in the observed interaural cues, particularly those dependent on spectral information. The results show that the neural network performs worse when analysing the reflected components. In this case, the reflected component measured with the KU100 is more accurately localised, with lower maximum error, relative error, root mean squared error and number of front-back confusions. Comparisons between the accuracy of the proposed method with the baseline shows that the NN is capable of reaching a higher degree of accuracy, with lower angular error and fewer front-back confusions. In Figure 6, comparisons between the direct sound and reflected component for BRIRs captured with the KEMAR 45 BC are presented. The boxplots show that for the direct sound, a maximum error of 12\({}^{\circ}\) and median error of 5\({}^{\circ}\) (mean error of 4.20\({}^{\circ}\)) were observed, while the reflected component has a maximum error of 30\({}^{\circ}\) and median of 8.5\({}^{\circ}\) (mean error of 10.87\({}^{\circ}\)). There is a significant difference in the neural network performance between the direct sound and reflected component, ANOVA(F(1,286) = 83.99, \(p<0.01\)). This observed difference could result from the difference in signal path distance, which was found to reduce prediction accuracy in [9; 10]. [PERSON] et al.