A lightweight CNN–transformer hybrid architecture with channel attention for real-time hazardous acoustic event detection

IntroductionHazardous acoustic event detection is critically important for intelligent surveillance, emergency response systems, and public safety monitoring applications. Accurate and real-time identification of dangerous sound events such as explosions, alarms, screaming, and weapon-related sounds can significantly improve situational awareness and accelerate emergency response in safety-critical environments.MethodsThis study proposes a lightweight deep learning architecture for hazardous sou