# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 3 Results
### Experiment 1  
Table 4 summarizes the results obtained using a single model, trained and tested on the whole audio corpus (synthesized with all 13 BRIR sets). A classification accuracy reached the highest accuracy, equal to 81.2%, during the training of the model with all the features. However, the accuracy achieved upon testing was slightly lower and amounted to 76.9%. This outcome indicated that the model was slightly over-fit during the training. According to the results obtained upon testing, binaural cues played a dominant role in terms of the identification of the spatial scenes (accuracy of 73.1%), followed by MFCCs (69.0%), spectral features (52.2%), and RMS-based metrics (39.9%). The difference in accuracy values reached for the binaural metrics and the MFCCs, respectively, was statistically significant at \(p=0.013\) level, whereas the differences between the accuracy values obtained for MFCCs, spectral features, and RMS-based metrics were statistically significant at \(p<10^{-11}\) level. Note that in this study the no-information rate was equal to 33.3%. Hence, the use of the RMS-based metrics in isolation from the other features yielded only a small improvement over the baseline value of 33.3%, with the accuracy being equal to 39.9%. This improvement was statistically significant at \(p=2.8\times 10^{-8}\) level. The values of the hyper-parameters of the LASSO model (alpha and lambda), tuned during the cross-validation procedure, were equal to 0.55 and \(3.094\times 10^{-3}\), respectively. An interesting observation could be made by inspecting the data presented in Tables 5 and 6. The tables present the accuracy results obtained during the training and testing procedures, respectively. In contrast to previously discussed Table 4, these tables illustrated the classification accuracy achieved using 13 BRIR-specific models. Each of the models was trained and tested with a different BRIR set. For example, the first row in Table 5 showed the performance of the model upon training using 336 audio excerpts synthesized with the BRIR set No. 1 (sbs). Similarly, the first row in Table 6 illustrated the accuracy obtained upon testing with the aforementioned model. Note that during the testing, another set of 120 stimuli was used. However, they were synthesized using the same BRIRs as upon training. It can be seen that the overall performance of the BRIR-specific models was markedly better compared to those obtained using a single model. According to the last column in Table 5, the classification accuracy of the individual models obtained during the training ranged from 71% to 97.8%. The accuracy of the BRIR-specific models reached upon testing varied from 74.2% to 98.3% (see the last column in Table 6). These outcomes implied that the classification model learned well the cues important for the discrimination between the spatial scenes within each BRIR set, however, it struggled to identify universal features required by a single, generic model. A closer examination of the data presented in Tables 5 and 6 revealed that for most of the models, the binaural cues played the most important role, in terms of the classification accuracy, followed by the MFCCs. The exceptions were the models obtained for the last two BRIR sets (Nos. 12 and 13) and the calypso BRIR set (No. 7). According to the statistical test of proportions, MFCCs were more prominent than the binaural cues (\(p=8.9\times 10^{-3}\)) for the model obtained for the BRIR set No. 12 (tuburo250). In the case of the models derived using the BRIR sets Nos. 7 and 13 (tuburo310 and calypso), no difference between the binaural cues and MFCCs could be statistically proven (\(p\) values equal to 1.0 and 0.56, respectively). Hence, it could be concluded that for the aforementioned two models, the MFCCs and the binaural cues were of similar importance.