LIVE EVENT DETECTION FOR PEOPLE’S SAFETY USING NLP AND DEEP LEARNING

Abstract

In today’s world, personal safety in environments such as remote or isolated areas, where individuals may be working alone, has become a critical concern. Threats such as robbery, assault, and other criminal activities are often accompanied by specific sounds, which can serve as early indicators of potential danger. While traditional security systems are available, they often fail to detect or classify these sounds with the necessary accuracy or in realtime. This project aims to address this challenge by developing a system that classifies different types of surrounding audio events, allowing for a deeper understanding of the environment in realtime. The focus of this project is on accurately detecting and classifying various audio signals, which may include common environmental sounds such as footsteps, vehicle noise, or background chatter. By applying a deep learning model, specifically a 1D Convolutional Neural Network (CNN), the system processes audio data from realworld environments to classify these sounds into distinct categories. The 1D-CNN model is well-suited for this task, as it can effectively capture timedependent features from the audio signals. The model is trained using a dataset of labeled audio events, where each audio clip is associated with a specific sound category. The deep learning model analyzes these signals, extracting key features that help distinguish between different audio events. This approach offers a powerful tool for understanding environmental audio in various settings, such as urban areas, workplaces, or isolated locations. By focusing on real-time sound classification, this project contributes to improving situational awareness and providing a foundation for further advancements in sound-based monitoring and analysis systems.