Paper Contents
Abstract
The increasing rate of crimes, especially in urban areas, calls for the development of advanced technologies to ensure public safety and timely intervention. Crime Prevention through Scream Detection, presents an Al-powered system designed to detect human screams in real-time using audio analysis techniques. The primary objective is to identify distress situations, such as assaults or emergencies, where a scream is likely to occur, and automatically trigger alerts to nearby authorities or security systems. The system employs machine learning models trained on a dataset containing both positive (scream) and negative (non-scream) audio samples. By extracting relevant features such as pitch, frequency, and amplitude, the model effectively differentiates between normal environmental sounds and actual screams. Once a scream is detected, the system can be configured to activate alarms, notify law enforcement, or record the event for further investigation. The solution is suitable for integration with smart surveillance cameras, home security devices, and mobile applications, making it versatile for use in homes, public spaces, transport systems, and institutions. Real-time detection enables faster response, potentially preventing crimes or reducing their severity. It demonstrates how artificial intelligence and sound recognition can work together to enhance existing security measures.
Copyright
Copyright © 2025 Sudharsan G. This is an open access article distributed under the Creative Commons Attribution License.
