## 5 Classification Experiments
### Classification of the Supervised Dataset
#### 5.2.1 Baseline Results for Coarse Classification  
We computed the metrics to evaluate the performance for the four-category taxonomy. We have calculated the results both at fragment level and for the entire audio clip. We applied majority voting without modification as \"Dog\" and \"Impact\" labels are not present in the four-category taxonomy. Table 3 shows the mean and standard deviation of the cross-validation iterations of the recall values (per class and macro-averaged) for each system at fragment level.  
Although the differences between the models are not statistically significant (confidence intervals for \(p\) > 0.95: ConvBlock 4.9, ResNet 4.9, AST 2.1), ResNet achieves the best overall result.  
The results for all the most represented categories are very good for the three systems, but the performance decays for the mechanical sounds category due to the scarcity of training material for this category and the different kind of sounds that are included in it.  
Table 4 shows the metrics for the entire audio classification. In this task, the overall performance of the systems is worse compared to the fragment classification scores. This is due to several factors: first, the number of audio clips is smaller than the number of frames and this produces greater variability in the results for each iteration. Second, in the classes where frame classification accuracy is worse (e.g., in short, punctual events) the frames correctly classified can be easily outnumbered by frames wrongly classified in other classes. This is the case for \"Mechanical\" and to a lesser extent for \"Nature\". It would be worth  
\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 & 94.2 \(\pm\) 1.9 & 95.7 \(\pm\) 2.5 & 94.7 \(\pm\) 1.8 \\ human & 97.7 \(\pm\) 0.4 & 97.9 \(\pm\) 0.5 & 97.4 \(\pm\) 0.8 \\ nature & 84.2 \(\pm\) 4.3 & 84.7 \(\pm\) 0.4 & 86.1 \(\pm\) 3.4 \\ mechanical & 53.9 \(\pm\) 18.3 & 53.2 \(\pm\) 18.4 & 51.5 \(\pm\) 11.3 \\ \hline Macro recall & 82.5 \(\pm\) 5.7 & 82.9 \(\pm\) 5.6 & 82.4 \(\pm\) 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recall values (mean and standard deviation) at fragment level classification averaged through the cross-validation iterations. Data in %.  
evaluating other methods apart from majority voting to see if this performance can be improved. Conversely the classes that have higher accuracy at fragment level are boosted because the majority voting filters out the sporadic fragment-level classification errors.  
The results were similar for both ConvBlock and AST in most of the categories, but for ResNet, the performance reduced more due to the fall in the most difficult classes (\"Nature\" and \"Mechanical\"). Nevertheless, regarding the macro recall, the difference between the models was not statistically significant (confidence intervals for \(p>0.95\): ConvBlock 4.3, ResNet 4.3, AST 4.7).  
Figure 5 shows the confusion matrix of the ResNet architecture for which we achieved the best results at the fragment level. The rows represent the true labels while the columns represent the predicted ones. The global confusion matrix was calculated considering the classifications obtained in all the iterations by each class and divided by the total number of elements in that class. Darker colors represent the highest scores.  
The results were good for all the categories except for the \"Mechanical\" one, for which 25.1% and 14.9% of the fragments were erroneously classified as \"Traffic\" and \"Human\", respectively. The differences between the values of the diagonal of the confusion matrix (Figure 5) and the values of Table 3, corresponding to the ResNet, were due to the fact that the former was calculated by adding all the classification results for the frames of every iteration, whereas the latter was calculated averaging the recalls of each iteration.