## 3 Results
reported that as source-receiver distances increased, and therefore the signal level relative to the noise floor or room reverberation decreased, the accuracy of the GMM predictions decreased. They reported that, averaged over seven reverb times, the number of anomalous predictions made by the GMM increased by \(\sim\)9% between a source-receiver distance of 2 m compared to a source-receiver distance of 1 m. Further causes of error could be due to system misalignment at point of measurement or lower signal-to-noise ratios (SNR) occurring due to signal absorption at the reflector and larger propagation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Head**} & \multirow{2}{*}{**Exact**} & \multirow{2}{*}{**Within \(\pm 1^{\circ}\)**} & \multirow{2}{*}{**Within \(\pm 5^{\circ}\)**} & \multirow{2}{*}{**Front-Back**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Root Mean**} \\  & & & & & & & **Squared Error** \\ \hline \multicolumn{8}{c}{**Cascade-Correlation Neural Network**} \\ \hline \multicolumn{8}{c}{**Direct Component**} \\ KEMAR & 17.36\% & 21.53\% & 64.58\% & 1.39\% & 7.10\% & 5.18\({}^{\circ}\) \\ KU100 & 13.19\% & 17.36\% & 68.06\% & 0\% & 6.90\% & 6.86\({}^{\circ}\) \\ \hline \multicolumn{8}{c}{**Reflected Component**} \\ \hline KEMAR & 2.08\% & 11.11\% & 32.64\% & 9.03\% & 23.61\% & 13.59\({}^{\circ}\) \\ KU100 & 0\% & 9.03\% & 37.50\% & 2.78\% & 15.43\% & 8.85\({}^{\circ}\) \\ \hline \multicolumn{8}{c}{**Baseline Method**} \\ \hline \multicolumn{8}{c}{**Direct Component**} \\ KEMAR & 1.39\% & 2.78\% & 11.81\% & 49.31\% & 38.78\% & 66.37\({}^{\circ}\) \\ KU100 & 1.39\% & 3.47\% & 13.19\% & 50\% & 36.01\% & 65.66\({}^{\circ}\) \\ \hline \multicolumn{8}{c}{**Reflected Component**} \\ \hline KEMAR & 0\% & 2.78\% & 11.11\% & 49.31\% & 38.85\% & 67.31\({}^{\circ}\) \\ KU100 & 0\% & 4.86\% & 21.53\% & 49.31\% & 36.81\% & 70.23\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Direction of arrival accuracy comparison for the direct sound and reflected components measured with the KEMAR and KU100 binaural dummy heads, for both the cascade-correlation neural network and the baseline method. path (source-reflector-receiver); an average SNR of approximately 22.40 dB and 13.14 dB was observed across the direct and reflected component, respectively. In Figure 7, the comparison between direct sound and reflected component for BRIRs captured using the KU100 are presented. The boxplots show that for the direct sound, a maximum error of 23\({}^{\circ}\) is observed and a median error of 5\({}^{\circ}\) (mean error of 5.15\({}^{\circ}\)), and the reflected component had a maximum error of 19\({}^{\circ}\) and median of 7\({}^{\circ}\) (mean error of 7.51\({}^{\circ}\)). Although the maximum and median errors are not too dissimilar between the predictions for the direct sound and reflected component, there is a significant difference in the distribution of the angular errors, ANOVA(F(1,286) = 18.85, \(p\) < 0.01). The direct sound DoA predictions are generally more accurate than those for the reflected component. As with the findings for the KEMAR, this could be due to the difference in signal paths between the direct sound and reflected component, system misalignment or lower SNR; an average SNR of approximately 22.41 dB and 10.91 dB was observed across direct sound and reflected components, respectively. In Figure 8, the comparison between the two binaural dummy heads is presented for both the direct sound and reflected components of the BRIRs. The box plots show that there is no significant difference between the medians for the direct sound, and while the maximum error observed for DoA predictions with the KU100 is higher than that of the KEMAR, there is no significant difference in the angular errors between the two binaural dummy heads, ANOVA(F(1,286) = 4.29, \(p\) = 0.04).