## 3 Results
This interpretation is corroborated by Figure 5, which shows elevation estimates extracted using Equation (3) from 30-s segments of Beach, Quiet Street and Train Station recordings as heat maps for comparison. The Beach and Quiet Street plots both show large areas across time and frequency where elevation estimates remain broadly consistent at around 90 deg, indicating the presence  
Figure 2: Mean and standard deviation classification accuracies across all folds for the entire dataset using various different feature sets (from [39]). MFCC: Mel-frequency cepstral coefficient; DirAC: directional audio coding. of broadband noise sources dominating around that angle. The Train Station plot, on the other hand, shows much more erratic changes in elevation estimates across time, and indeed there is no confusion between Beach and Train Station using the E/D classifier. Figure 4: Confusion matrices of classifiers trained using MFCC features and elevation/diffuseness features extracted using DirAC. Figures indicate classification percentages across all folds (from [39]). Figure 3: Mean and standard deviation classification accuracies across all folds for each scene class using the elevation/diffuseness–Gaussian mixture model (E/D-GMM) classifier. It is interesting to consider instances where the E/D classifier considerably outperforms the MFCC classifier, such as with Pedestrian Zone, which is classified with 97% accuracy by the E/D classifier, whereas the MFCC classifier only manages 52%. This indicates that the spatial information present in pedestrian zones is much more discriminative than the spectral information, which seems to share common features with both quiet streets and train stations. Further to this, it is interesting to investigate  
Figure 5: Heat maps depicting elevation estimates extracted from 30-s segments of Beach, Quiet Street and Train Station recordings. the instances where there is significant confusion present in both classifiers. Park, for instance, is most commonly misclassified as Quiet Street by both classifiers. This is probably due to the fact that both Park and Quiet Street scenes are both characterised as being relatively quiet locations, yet are still in the midst of urban areas. These recordings tend to contain occasional human sound and low-level background urban 'hum' (as opposed to Woodland, which tends to lack this). In other cases, however, the specific misclassifications do not always correspond. The most common misclassification of the Shopping Centre by the MFCC classifier is the Pedestrian Zone, a result perhaps caused by prominent human sound found in both locations. In contrast to this, for the E/D classifier the most common misclassification of the Shopping Centre is Train Station, and in fact there is no confusion with the Pedestrian Zone at all. This could be due to the similarity in acoustics between the large reverberant indoor spaces typical of train stations and shopping centres, which could have an impact on the values calculated for elevation and diffuseness. Figure 6 shows receiver operating characteristic (ROC) curves for the individual models trained to identify each location class. These curves evaluate each GMM's performance as a one-versus-rest classifier. The curves were generated by comparing the scores generated by each model with the ground-truth labels for each scene and calculating the probabilities that a certain score will be given to a correct clip (true positive) or will be given to a clip from another scene (false positive). These pairs of probabilities are calculated for every score output from the classifier and when plotted, form the ROC curve. The larger the area under the curve (AUC), the better the classifier. The curves shown in Figure 6 show the mean ROC across the four folds. It can clearly be seen that the AUC values do not follow the pattern of the classification accuracies shown in Figure 3. This discrepancy is most stark in Figure 5(a), which shows the Beach model to be the best individual classifier, with an AUC of 0.95.