## 5 Classification Experiments
### Classification of the Supervised Dataset
#### 5.2.2 Baseline Results for Fine Label Classification  
We also trained the classifiers with the nine-category taxonomy, making decisions both at fragment level and for the entire audio clip. In this last case, we used the modified majority voting for \"Dog\" and \"Impact\" categories. Table 5 shows the mean and standard deviation for all the iterations of the recall values (per class and macro-averaged) at the fragment level. Once again, the difference between the models was not statistically significant (confidence intervals for \(p>0.95\), ConvBlock 3.9, ResNet 4.4, AST 3.2) but the best macro-average recall was obtained for the AST system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **ConvBlock (m \(\pm\)\(\sigma\))** & **ResNet (m \(\pm\)\(\sigma\))** & **AST (m \(\pm\)\(\sigma\))** \\ \hline Traffic & 97.3 \(\pm\) 1.7 & 98.3 \(\pm\) 0.9 & 98.6 \(\pm\) 1.9 \\ Human & 83.5 \(\pm\) 3.1 & 87.0 \(\pm\) 4.4 & 84.9 \(\pm\) 5.3 \\ Nature & 63.3 \(\pm\) 12.5 & 60.0 \(\pm\) 17.0 & 70.0 \(\pm\) 12.5 \\ Mechanical & 41.4 \(\pm\) 11.4 & 29.5 \(\pm\) 9.2 & 41.8 \(\pm\) 15.1 \\ \hline Macro recall & 71.4 \(\pm\) 4.9 & 68.7 \(\pm\) 4.4 & 73.8 \(\pm\) 5.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recall values (mean and standard deviation) for the entire audio clip classification averaged through the cross-validation iterations. Data in %. Figure 5: Overall confusion matrix for ResNet at the fragment level. Data in %. In general, the performance was good for most of the classes. The main exceptions were the \"Dog\" and especially the \"Impact\" classes, which had a very bad recall. This is due to the scarcity of samples of this class and to the different kind of events (beats, fiercrackers, etc.) grouped under this label. The AST architecture performed better in these categories, probably because it benefited from the transfer learning to compensate the lack of training material. It is worth noting that if we exclude the \"Impact\" category from the calculus of the global recalls, the results are very similar for all the systems: ConvBlock 71.9, ResNet 71.1 and AST 72.5. Table 6 shows the metrics for the entire audio clip classification. The performance in this case improves slightly for the ConvBlock compared to the fragment-by-fragment classification, but decays for the others. The effects explained in the four-category case for the entire audio clip classification, namely the small number of events of some classes and the dispersion of results depending of the accuracy level of the class, apply also here. It is worth mentioning the effect of the modified majority voting in the \"Dog\" and \"Impact\" categories which boosts their accuracy in some cases. Overall, ConvBlock was the best performing system, although the difference between the models was not statistically significant (confidence intervals for \(p\) > 0.95, ConvBlock 4.4, ResNet 6.3, AST 3.1). Finally, in Figure 6, we present the overall confusion matrix at fragment level for the best system, i.e., the AST architecture. The rows of the matrix represent the true labels while the columns show the predictions. Darker colors represent the highest scores. It can be said that the system classifies well the \"Storm\", \"Voice\", \"Cleaning-truck\" and \"Motorbike\" categories. On the contrary, the \"Impact\" category presents the worst classification rate with 14.2% of correct answers. Among the categories belonging to the traffic group (car, motorbike and cleaning truck), it is observed that the fragments that have not been correctly