Emerging Trends in Artificial Intelligence for Dangerous Sound Event Detection: Challenges and Future Perspectives

Abstract

This survey provides a comprehensive overview of emerging trends in artificial intelligence for dangerous sound event detection, emphasizing recent advances, existing challenges, and future research directions. Dangerous acoustic events such as explosions, gunshots, and screams represent critical cues for public safety, requiring intelligent systems capable of rapid and reliable detection under real-world conditions. The paper reviews the evolution of sound event detection pipelines from traditional feature-based models to deep learning approaches including convolutional, recurrent, and transformer architectures. It highlights multimodal fusion techniques that integrate audio with visual and contextual data, enabling robust event recognition in complex and noisy environments. Key challenges such as data scarcity, environmental variability, real-time processing constraints, and ethical considerations are analyzed in depth. The review also explores promising directions including self-supervised learning, federated intelligence, explainable AI, and edge-based deployment for scalable, privacy-preserving applications. Comparative analysis reveals that hybrid and attention-based architectures deliver superior generalization, paving the way for context-aware, autonomous, and ethically responsible sound detection frameworks. Ultimately, this survey contributes to a deeper understanding of the technological and societal implications of AI-driven acoustic intelligence, guiding future innovation in security, surveillance, and emergency response systems