## 4 Discussion  
The results presented in Section 3 indicate that the collation of EigenScape has been successful in that this classification exercise shows the suitability of this dataset for segmentation and cross-validation. The good, but not perfect, degree of accuracy shown by the baseline E/D-GMM classifier is very significant in that it goes some way towards showing the validity of this dataset in terms of providing a good variety of recordings. Recordings within a class label are similar enough to be grouped together by a classifier, whilst retaining an appropriate degree of variation. These results suggest that DirAC spatial features extracted from Ambisonic audio could be viable and useful features to use for acoustic scene identification. The simplicity of the classifier used here indicates that higher accuracies could be gleaned from these features, perhaps by using a more sophisticated decision-making process, or simply more sophisticated models. Utilising  
Figure 6: Receiver operating characteristics (ROC) curves for each scene classifier, showing mean (solid line) and standard deviation (grey area) of the curves calculated using results across all folds. Dotted line represents chance performance. AUC: area under the curve. temporal features could be a compelling next step in this work. It would be especially interesting to investigate whether \(\Delta\)-azimuth values could be more discriminative than the azimuth values themselves, being perhaps less dependent on microphone orientation. It is also worth noting that all spatial analysis of this dataset so far has used only the first-order Ambsonic channels for feature extraction. The fourth-order channels present in this database provide much higher spatial precision that could enable more sophisticated feature extraction. The high channel count should also facilitate detailed source separation that could be used for polyphonic event detection work. Event detection within scenes should be a key area of research with this dataset moving forwards. The size and scope of this database are such that there is a lot more knowledge to be gained than has been presented here. The findings of this paper are important initial results that indicate the investigation of spatial audio features could be a fertile new area in machine listening, especially with a view to applications in environmental sound monitoring and analysis. The EigenScape database is provided freely to inspire and promote research work and creativity. Please cite this paper in any published research or other work utilising this dataset. EigenScape Dataset: [[http://doi.org/10.5281/zenodo.1012809](http://doi.org/10.5281/zenodo.1012809)]([http://doi.org/10.5281/zenodo.1012809](http://doi.org/10.5281/zenodo.1012809)); Baseline code and segmentation tools: [[https://github.com/marc1701/EigenScape](https://github.com/marc1701/EigenScape)]([https://github.com/marc1701/EigenScape](https://github.com/marc1701/EigenScape)); Recording Map: [[http://bit.ly/EigenSMap](http://bit.ly/EigenSMap)]([http://bit.ly/EigenSMap](http://bit.ly/EigenSMap)). Funding was provided by a UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Award, the Department of Electronic Engineering at the University of York, and in part by Digital Creativity Labs, EPSRC Grant Number: EP/M023265/1. [PERSON] conducted the recording work, created the baseline classifier, conducted analysis and wrote the paper. [PERSON] supervised the project, provided initial ideas and guidance and has acted as editor and secondary contributor to this paper. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: