Human Scream Detection and Analysis to Control Crime Rate using Machine Learning

This research explores the detection of human screams using acoustic analysis. By employing machine learning techniques, we aim to distinguish screams from ambient noise, providing a valuable tool for various applications such as public safety, emergency response, and healthcare. Our approach involves feature extraction and classification to enhance the accuracy of scream detection systems, contributing to improved real-time recognition and response mechanisms. This research contributes to the evolving field of audio analysis and machine learning by presenting a comprehensive approach to human scream detection. The developed system’s potential applications in emergency response, public safety, and mental health make it a valuable asset in diverse domains. The findings underscore the importance of combining technological advancements with ethical considerations to ensure responsible and beneficial deployment in real-world scenarios .The study explores the ethical considerations surrounding the deployment of such technology.