## 4 Discussion  
The evaluation of the algorithm on different data sets yielded promising results in terms of key performance metrics. In Data Set 1, high precision was achieved in detecting \"Cries\" and \"Voices\" at 92.5% and 88.0%, respectively. The completeness was also notable, reaching 90.2% for \"Screams\" and 85.7% for \"Voices\". These figures indicate that the algorithm can effectively identify these sound events' presence in audio signals. However, it was observed that the algorithm had a slightly lower performance in detecting \"Whispers\", with an accuracy of 78.6% and a completeness of 82.3%. This suggests that whispering may present specific challenges to the algorithm, possibly due to its soft, low-amplitude nature. Regarding the \"Noise Background\", the algorithm achieved a high level of accuracy of 94.7% but a lower completeness of 87.0%. It is important to note that, in this case, the priority is to reduce false positives since incorrectly labeling the noise as a sound event can hurt the application of the algorithm. Compared with the works reviewed in the literature, the developed algorithm shows competitive performance in detecting specific sound events. In the study [16], which focused on the detection of \"Baby crying\", an accuracy of 91% and a completeness of 85% were achieved, values that are in line with those obtained in the present algorithm for similar events such as \"Screams\" and \"Voices\". Another relevant work is [35], which addressed the detection of \"Siren Sounds\" in urban environments. While the context is different, it is interesting that their algorithm achieved 88% accuracy and 92% completeness. These results are also comparable to the metrics obtained for the sound events evaluated in the present study. However, it is essential to highlight that direct comparison with reviewed works should be considered cautiously due to differences in data sets, experimental conditions, and categories of sound events evaluated. Each context and data set can present unique \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Valor** \\ \hline Precision & 97.7\% \\ Sensitivity & 96.9\% \\ Specificity & 98.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results in the detection of broken glass. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Sensitivity** & **Specificity** \\ \hline CNN Proposal & 98.5\% & 97.8\% & 99.2\% \\ MTBC & 92.3\% & 90.1\% & 94.7\% \\ RNN & 94.5\% & 92.8\% & 95.6\% \\ CNN 1D & 95.7\% & 94.3\% & 96.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of methods for the detection of acoustic events. challenges that influence the algorithm's performance--analysis of false positives and negatives revealed areas where the algorithm could improve. It was observed that false positives for \"Screams\" and \"Voices\" often originated from high-amplitude segments of sudden sounds, such as clapping or banging. A possible improvement strategy would be implementing additional preprocessing to detect these amplitude spikes and reduce their influence on detection. Regarding false negatives, it was identified that \"Whispers\" and \"Noise Background\" were sometimes not detected due to their low-amplitude nature. One strategy could be to adjust the detection thresholds precisely for these events, considering their amplitude and frequency characteristics. Furthermore, incorporating data augmentation techniques during training could help the algorithm become more robust to variations in amplitude and background noise [36]. The learning curves revealed how the algorithm's performance improved as the training set's size increased. It was observed that the performance stabilized faster for sound events with enough examples in the training set. However, for events with a limited number of models, such as \"Whispers\", the algorithm benefited significantly from an increase in the size of the training set.