## 4 Results  
The results of the calculations of the hourly average values of \(\overline{\text{J}}\) for the ten days of activity in the experiments are summarized in Table 1. We present these temporal variations in four six-hour periods, with an early morning peak between 00:00 a.m. and 05:00 a.m., a morning peak between 06:00 a.m. and 11:00 a.m., an evening peak between 12:00 p.m. and 17:00 p.m. and a nighttime peak between 18:00 p.m. and 23:00 p.m.  
Additionally, we disaggregated the temporal evolution of the classes by its geographical origin (wetland), as seen in Figure 8. We maintain the same time-periods used in Table 1 to facilitate comparison of the information among the habitats.  
Figure 9 shows the two dimensional embedding obtained by applying the procedure described in Section 3.6 over the complete dataset of mbe features. The upper and lower subfigures show the embeddings of the frames colored by their geographical origin and labeled single-event, respectively.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Jaccard Coefficients** & \multicolumn{4}{c}{**Periods (hours)**} & \multirow{2}{*}{**Mean**} \\ \cline{2-2} \cline{4-6}
**between Wetlands** & & & & & \\ \cline{2-2} \cline{4-6}
**Angachilla–Miraflores** & 0.415 & 0.526 & 0.428 & 0.402 & 0.443 \\ Angachilla–El Bosque & 0.392 & 0.531 & 0.459 & 0.449 & 0.458 \\ Miraflores–El Bosque & 0.359 & 0.556 & 0.443 & 0.414 & 0.443 \\ \hline \end{tabular}
\end{table}
Table 1: Temporal variations of average Jaccard coefficient \(\overline{\text{J}}\) in four periods of six hours.  
Figure 8: Average temporal evolution of sonic events present in each wetland, obtained from field recordings.  
The quality of the experimental results of SEDnet is assessed by using the F1-score and error rate metrics. We conducted exhaustive experiments to compare the detection performance of SEDnet by tuning the hyper-parameters and the number of audio files for training. The results presented here are based on the best set of hyper-parameters, which are as follows:  
* Learning rate: 0.001;
* Batch size: 32;
* Dropout probability: 0.5;
* Sequence length: 1024 frames.  
An example of the training and validation learning curves and the evolution of the performance metrics during training is shown in Figure 10.  
The average values of the F1-score and the error rate obtained from the four folds are provided in Table 2. In addition, for the best fold (according to training), we show the F1-score and error rate.  
Figure 11 shows a comparison between the ground truth of a 5 min recording from the test set and the corresponding SEDnet prediction using the best model from Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SEDnet** & **F1-Score** & **Error Rate** \\ \hline Average 4-folds & 0.784 & 0.391 \\ Best fold & 0.790 & 0.377 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average F1-score and average error rate for the best fold and average labeling time.  
Figure 10: Learning curves for the best set of parameters.  
Figure 9: Two-dimensional embedding of UMAP with visual acoustic features for three habitats (**left**) and for the five single-events of reference where multi-events were absent (**right**).  
All the results presented so far were obtained with the default threshold of 0.5. We explore the performance as a function of the threshold or operating points using PSD-ROC curves. Figure 12 shows the PSD-ROC curves per sound event (colored solid lines) and the average PSD-ROC curve (black dashed line). Table 3 shows the areas under these PSD-ROC curves.  
Finally, in terms of computational efficiency, the best model was able to detect sound events from an audio file (5 min) in a time of 14.49 \(\pm\) 0.17 s.