AI-based human scream detection for crime prevention

The Human Scream Detection System is a next-generation safety intelligence framework, which, when completed, will autonomously identify and react to expressions of distress, in particular screams, from within live audio streams. Screams are universal acoustics, linked to fear, pain, or perceived threat, which are often expressed by a human being in any language and culture. For this reason, screams have great potential as a means through which an automated threat might be detected. This system combines high-level audio signal processing with machine learning and deep learning models to separate high-intensity human vocalizations from background noise. The main modules critical to its functioning are preprocessing robust to noise, spectral-temporal feature extraction using MFCC and pitch contours, and classification with SVM, MLP, and CNN models. On the autonomous detection of a scream event, this system can automatically raise emergency alerts and send geolocation information to nearby responding units through secure APIs like Twilio. Other areas of application include smart home security, patient distress monitoring, and occupational safety. This review paper will attempt to critically assess scream detection systems, covering their design paradigms, operational performance, and ethical issues, and will discuss multimodal sensor fusion, adaptive learning, and privacy-preserving real-time deployment in further directions for scalable public safety solutions