## 6 Discussion  
The accuracy reached by human listeners in the task of the classification of spatial audio scenes was low, being equal to 42.5% for all the listeners and to almost 60% for the listeners who passed the post-screening test. Such a low accuracy rate could be attributed to the difficulty faced by the listeners under the static-head scenario. The literature shows that the rate of front-back discrimination errors is large when the users of binaural audio systems cannot exploit the mechanism of the head movements [18]. Moreover, the classification task was also hindered by an uncontrolled electro-acoustical environment (background noise, headphones frequency response, no free-field headphone compensation). While this feature of the listening test could be considered as the significant limitation of the study, the obtained results represent a typical scenario of an ordinary listener auditioning binaural music recordings over the Internet under \"realistic\" conditions (enhanced ecological validity). The outcome of the comparison between the classification accuracy reached by human listeners against those obtained by the machine audition algorithms depends on the way the algorithms are tested. Under the BRIR matched conditions, the classification algorithms outperform the listeners. However, under the BRIR mismatched conditions, the performance of the machine audition algorithms is comparable or even slightly worse than the level exhibited by the listeners who passed the post-screening test. Hence, the generalizability of the algorithms, understood as their ability to perform in unknown electro-acoustic conditions, needs to be further improved, which constitutes the direction for future work. It is difficult to compare the results obtained using the machine learning algorithms with those reported in the literature, since the automatic classification of spatial audio scenes evoked by binaural audio recordings is a relatively new topic, constituting an emerging field of research. For the best performing algorithm (SVM), the classification results presented in this paper are 7% better than those obtained using a least absolute shrinkage and selection operator (LASSO) in our previous work [13] (\(p=2.4\times 10^{-6}\)), under the BRIR matched conditions. However, when the classification algorithms are tested under the BRIR mismatched scenario, the outcomes of this and the former study are almost identical, with the classification accuracy being equal to 56.7% and 56.8%, respectively (the difference is not statistically significant, \(p=0.94\)). The results obtained for the CNN algorithm appear to be somewhat disappointing as they are comparable to the outcomes obtained by the traditional algorithms (see Fig. 7). It was expected that this deep learning algorithm would substantially outperform the three traditional classification algorithms, since, in general, deep learning methods provide better accuracy performance than traditional machine learning classification algorithms [46]. One possible reason for the comparatively low performance of the CNN algorithm could be attributed to a relatively small training dataset, consisting of 3696 audio recordings, thus yielding 1232 training items per audio scene. Such a number of training items may be insufficient to train the network due to the risk of overfitting, considering the large number of its trainable parameters (more than 8 million). However, it is known that CNN algorithms are capable of reaching satisfactory classification results even when trained on relatively small datasets, containing 1000 items per class [48]. Moreover, the performance of the CNN algorithms can be further boosted by incorporating various forms of data augmentation [46; 48].