Robust Threat Identification through Real-Time Audio Event Detection with 1D CNNs

In today’s world, ensuring personal safety especially in remote or isolated areas where people often work alone is more important than ever. In such environments, threats like robbery or assault often come with certain sounds that can act as early warning signs. Unfortunately, traditional security systems aren’t always equipped to recognize or react to these sounds accurately or in real time. This paper tackles that challenge by developing an intelligent system that can listen to and classify sounds in the surrounding environment. Whether it’s footsteps approaching, a car engine revving, or background chatter, the system aims to detect these audio cues and identify what they might mean. At the heart of this system is a 1D Convolutional Neural Network (1D-CNN), a deep learning model that’s particularly good at understanding time-based signals like audio. By training the model on a labeled dataset of real-world sounds, it learns to pick up on patterns and features that distinguish one sound from another. Once trained, the model can process live audio, categorizing it into different sound types with high accuracy. This real-time sound classification offers a smarter way to understand what’s happening in the environment whether in a busy urban street, a quiet workplace. Ultimately, this project isn’t just about recognizing sounds it’s about enhancing awareness, improving safety, and laying the groundwork for smarter