# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 3 Results
### Experiment 2  
The three tests were made to examine whether the method was capable to generalize when it was trained and then tested on the stimuli synthesized using different BRIR sets, but of similar reverberation time. The obtained results are presented in Table 7. For the first condition (short reverberation time), the model was trained using the stimuli synthesized with the following BRIR sets: sbs, thkcr1, calypso, labtui, and tuburo310. Then, it was tested employing the stimuli synthesized with the thkct7 and tuburo250 BRIR sets. According to the results, the model exhibited a moderate level of generalizability, yielding an accuracy level of 65.4%. For the remaining two conditions (medium and long reverberation times), the accuracy was low and equaled 51.7% and 40.0%, respectively. The above outcomes indicated that the method showed a moderate level of generalizability for the BRIR sets of short reverberation time, but it failed to generalize for the BRIRs representing medium or long reverberation times. Other factors, extending beyond the acoustical conditions of the BRIR sets, may be of higher importance in terms of the generalizability of the method. For example, a surprisingly high classification accuracy, being equal to 82.5%, was obtained when the method was trained using the stimuli synthesized with the tuburo250 BRIR set and then tested on the recordings produced with the tuburo310 BRIR set. Although for these two BRIR sets the acoustical conditions were slightly different, in both cases the audio excerpts were generated using the wave-field synthesis method. This outcome indicated that the generalizability of the method may have strongly depended on the algorithm used to synthesize binaural audio recordings. In order to test the inter-BRIR generalizability of the method in a more systematic way, a \"leave-one-BRIR-out\" validation technique was adopted. The procedure was applied iteratively, excluding one BRIR set from training in each iteration. The obtained results are presented in Table 8. For example, in the first iteration, the sbs BRIR set was left out, and the model was trained on the stimuli synthesized with all the remaining BRIR sets (12 sets in total), reaching a training accuracy of 80.8%. Then, the model was tested on the recordings synthesized using the previously excluded BRIR set (sbs in this case), yielding a testing accuracy of 44.2% (see the first row in Table 8). The procedure was repeated for the remaining BRIR sets. According to the third column in Table 8, the training accuracy for all the models was high, with an average value of 81.2%. However, the models exhibited an overfitting effect, as the testing accuracy was substantially less than the training accuracy (see the last column in the table). The best results were obtained for the tvtui2 BRIR set, with a testing accuracy reaching 79.2%, followed by the tuburo310 BRIR set yielding a test accuracy equal to 73.3%. By contrast, a testing accuracy obtained for the calypso and rehabtui BRIR sets was statistically not different from the no-information rate of 33.3% (\(p=0.31\)). The average accuracy level obtained upon testing was equal to 56.8%. In addition to testing the generalizability of the method, Table 8 could also be used to examine which BRIR sets were the most challenging in terms of the classification accuracy. At the outset of the study, it was expected that the stimuli synthesized with the most reverberant impulse responses would be the most difficult to classify accurately. Moreover, it was presumed that spatial resolution (the number of measured impulse responses) or a type of a dummy head could also influence the results. However, according to Table 8, it was difficult to identify experimental factors responsible for the fact that the stimuli synthesized with some BRIRs were classified less accurately than the others. For example, contrary to expectation, the BRIR sets Nos. 1, 3, and 7 (sbs, thkcr1, and calypso) ranked among the worst classified cases despite their short reverberation times.