## 5 Conclusions  
In this study, a sound event detection algorithm has been presented and evaluated that demonstrates promising performance in identifying specific sound events. A comprehensive understanding of the algorithm's performance has been obtained through a combination of performance metrics, confusion matrices, false positive and false negative analysis, learning curves, and runtime evaluation. The sound event detection algorithm has the potential to be applied in a variety of contexts, from security and surveillance systems to environmental data analysis. Through careful iteration and refinement, the strategies identified to address false positives and negatives could further improve its performance. Also, continuous data collection and expansion of event categories could make the algorithm more robust and versatile.  
The evaluation results show that the algorithm can successfully detect sound events of different categories in the data set. Performance metrics such as precision, completeness, F1-score, and area under the ROC curve reveal a balance between detectability and minimizing false positives. However, it is noted that specific event categories present challenges, highlighting the importance of specific improvement strategies.  
Analysis of false positives and negatives has identified areas for refinement of the algorithm. It has been observed that low amplitude and frequency events can result in false negatives, suggesting the need for data augmentation and threshold adjustment techniques. On the other hand, events with similar characteristics to different categories can lead to false positives, indicating the importance of considering more advanced classification approaches. Learning curves have shown how the algorithm's performance improveswith the increasing size of the training set. This highlights the positive influence of the data on model performance and suggests that expanding the data set could lead to better performance.  
Runtime analysis reveals that the algorithm is suitable for applications that do not require real-time detection. However, for time-sensitive applications, additional optimizations might be necessary. It is important to note that this study focused on a specific set of sound events and was conducted under controlled conditions. Future research could extend the evaluation to additional events and test the algorithm in real-world settings to assess its performance in more complex and varied situations. Furthermore, incorporating advanced signal processing and deep learning techniques could significantly improve the accuracy and robustness of the algorithm. Based on the analysis of false positives and negatives, more specific strategies can be developed to address challenges in detecting specific categories of events. This could include the design of more advanced classification models or the exploration of more sophisticated thresholding techniques.  
Conceptualization, W.V.-C.; methodology, W.V.-C.; software, J.G.; validation, J.G.; formal analysis, W.V.-C.; investigation, J.G.; data curation, W.V.-C. and J.G.; writing--original draft preparation, J.G.; writing--review and editing, J.G.; visualization, W.V.-C.; supervision, W.V.-C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data used in this study were obtained from sources of the university participating. However, it is essential to note that the implemented source code for deep learning models is not publicly available due to intellectual property and copyright restrictions. Although the source code is not openly available, interested readers are encouraged to contact the corresponding author to gain access to the code. To request the source code, please send an email to [EMAIL_ADDRESS]. The author commits to making the code available to interested readers to foster collaboration and knowledge sharing in deep learning. The authors declare no conflict of interest.