A Survey on Intelligent Sound Event Detection Systems for Public Safety and Emergency Response

Abstract

This survey provides a comprehensive analysis of intelligent sound event detection (SED) systems developed for public safety and emergency response applications. The paper examines the theoretical foundations, deep learning architectures, datasets, and benchmarking methods that enable the detection and classification of complex acoustic events in real-world environments. Recent advances in convolutional, recurrent, and transformer-based neural networks have significantly improved the capability of SED systems to identify critical events such as gunshots, explosions, alarms, and distress sounds under noisy and dynamic conditions. The integration of artificial intelligence, Internet of Things (IoT) frameworks, and edge computing facilitates real-time sound analysis, reducing response latency and enhancing situational awareness for emergency management. Furthermore, the survey explores challenges related to data scarcity, environmental variability, and ethical considerations, emphasizing the necessity of explainable and privacy-preserving models. Comparative evaluations of existing architectures reveal performance trade-offs between accuracy, computational cost, and deployment feasibility. The study concludes by identifying future research directions, including adaptive learning, multimodal fusion, and decentralized processing. Collectively, intelligent SED systems represent a transformative approach to proactive safety monitoring, offering robust, scalable, and ethically guided solutions for next-generation smart city infrastructures