## 3 Results  
Initial analysis of this dataset previously published as part of the DCASE 2017 workshop [39] compared classification accuracies achieved using the DirAC features to those achieved when using MFCCs. In addition, classifiers were trained using individual DirAC features--azimuth, elevation and diffuseness--and a classifier was trained using a concatenation of all MFCC and DirAC features. Figure 2 shows the mean and standard deviation classification accuracies achieved across all scenes using these various feature sets. It can be seen that using all DirAC features to train a GMM classifier gives a mean accuracy of 64% across all scene classes, whereas MFCC features give a 58% mean accuracy (averaged across all folds). Azimuth data alone is much less discriminative between scenes, giving an accuracy of 43% on average, which is markedly worse than MFCCs. Elevation data, on the other hand, gives similar accuracies, and diffuseness data gives slightly better accuracies than MFCCs. The low accuracy when using azimuth data is probably attributable to the fact that azimuth estimates will be affected by the orientation of the microphone array relative to the recorded scene, whereas elevation and diffuseness should be rotation-invariant. A new classifier using elevation and diffuseness values only was therefore trained and gave an average classification accuracy of 69%, which is the best performance that was achieved. The elevation/diffuseness (E/D)--GMM classifier was therefore adopted as the baseline classifier and all further results reported here are derived from it Figure 3 shows the mean and standard deviation classification accuracies from the baseline for each acoustic scene class. As previously mentioned, the mean accuracy across all scene classes is 69%. The low standard deviation (7%) indicates the dataset as a whole gives features that are fairly consistent across all folds. All of the scene classes except Beach are classified with mean accuracies above 60%. In fact, if the Beach class is discounted, the overall mean accuracy rises by 9%. Busy Street, Pedestrian Zone and Woodland are classified particularly well, at 86%, 97% and 85% accuracy, respectively. Looking at the standard deviation values for accuracy across folds could give some indication of the within-class variability between the different scene recordings. The very low standard deviation in Pedestrian Zone accuracies of 4% implies that the Pedestrian Zone recordings have very similar sonic characteristics, that is, they give very consistent features. Busy Street, Park and Train Station could be said to be moderately consistent, whereas Quiet Street, Shopping Centre and Woodland show more variability between the various recordings. The drastically lower accuracy of the Beach scene classification is very anomalous. It could be that as the primary sound source at a beach will likely be widespread and diffuse broadband noise from the ocean waves, this could yield indistinct features that could be difficult for the classifier to separate from other scenes. Figure 4 shows confusion matrices (previously published in [39]), which indicate classifications made by the MFCC and E/D classifiers averaged across all folds. Rows indicate the true classes and columns indicate the labels returned by the classifiers. The E/D matrix features a much more prominent leading diagonal and confusion is much less widespread than in the MFCC matrix, clearly indicating that the E/D classifier outperforms the MFCC classifier in the vast majority of cases. Beach is the only class in which the MFCC classifier significantly outperforms the E/D classifier. The most commonly-returned labels for the Beach scene by the E/D classifier are Quiet Street and Busy Street, perhaps due to the aforementioned broadband noise from ocean waves yielding spatial features similar to that of passing cars.