## 8 Conclusions  
This work demonstrates that CNN is capable of undertaking the challenging task of identifying front, back, up, and down music ensembles in synthetically generated binaural signals, which constitutes the main contribution of this study to the field of spatial audio scene characterization (SASC). The classification accuracy scores obtained in this study could be considered satisfactory for narrow music ensembles (\(\pm 15^{\circ}\)), particularly when tested under the HRTF-dependent conditions. For these conditions, CNN yielded the classification accuracy equal to 90.7% (SD 1.0). The accuracy level decreased monotonically with the increase in the ensemble size, indicating that the method needs further improvements in terms of the classification of wider ensembles.  
The results obtained under the HRTF-independent tests are approximately 10 to 20 percentage points lower compared to those achieved under the HRTF-dependent tests, implying that the generalization property of the developed method also needs to be enhanced. This could be accomplished by increasing the number of different HRTF sets employed in the development process. The results also suggest that the corpus of the HRTFs should be more diversified in terms of the types of the artificial heads used for their measurements.  
In order to obtain a better insight as to how CNN undertook its classification process, a modified image occlusion sensitivity technique was applied. According to the results, several prominent frequency bands were identified in terms of the classification of the music ensembles (3.5-5 kHz for front ensembles, 1.5-3 kHz for back ensembles, 5.5-8 kHz for up ensembles, 0.5-1.5 kHz and 2.5-5 kHz for down ensembles). These frequency bands are largely in accordance with the psychoacoustical literature.  
Future work regarding spatial audio scene characterization (SASC) may involve extending the model through incorporating reverberant BRIRs and by relaxing the current limitation of the method regarding the circular shape of the music ensemble boundaries.  
Conceptualization and methodology, S.K.Z. and H.L.; visualization of music ensembles, P.A.; generation of the binaural audio repository, P.A.; design, implementation, training and testing of the convolutional neural network, P.A.; data analysis, P.A.; paper writing, S.K.Z. and H.L. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the grant from Bialystok University of Technology (WZ/WIIT/4/2020) and funded with resources for research by the Ministry of Science and Higher Education in Poland.  
Not applicable.  
Data Availability Statement: The binaural music excerpts, generated and exploited in this study, are not publicly available due to copyright restrictions but could be provided directly by the authors upon reasonable request. The trained CNN model along with the source code of the final models used in the study are publicly available at GitHub ([[https://github.com/pawel-antoniuk/appendix-4](https://github.com/pawel-antoniuk/appendix-4) scenes-localization-mdpi-2021]([https://github.com/pawel-antoniuk/appendix-4](https://github.com/pawel-antoniuk/appendix-4) scenes-localization-mdpi-2021) (accessed on 22 December 2021)).  
The authors declare no conflict of interest.