Application of Deep Learning in the Early Detection of Emergency Situations and Security Monitoring in Public Spaces

Abstract

This article addresses the need for early emergency detection and safety monitoring in public spaces using deep learning techniques. The problem of discerning relevant sound events in urban environments is identified, which is essential to respond quickly to possible incidents. To solve this, a method is proposed based on extracting acoustic features from captured audio signals and using a deep learning model trained with data collected both from the environment and from specialized libraries. The results show performance metrics such as precision, completeness, F1-score, and ROC-AUC curve and discuss detailed confusion matrices and false positive and negative analysis. Comparing this approach with related works highlights its effectiveness and potential in detecting sound events. The article identifies areas for future research, including incorporating real-world data and exploring more advanced neural architectures, and reaffirms the importance of deep learning in public safety.