Human Scream Detection and Analysis for Controlling Crime Rate

This research presents an automated human scream detection system designed to enhance public safety and contribute to crime reduction initiatives. The system utilizes deep learning techniques to accurately distinguish human screams from other environmental sounds, offering a potential early warning mechanism for emergency situations. The methodology employs Mel-frequency cepstral coefficients (MFCCs) for audio feature extraction and a bidirectional long short-term memory (BiLSTM) neural network architecture for classification. A dataset comprising labeled scream and non-scream audio samples was used to train and validate the model, achieving 92.5% accuracy on test data. Additionally, a graphical user interface was developed to facilitate real-time scream detection and visualization of audio waveforms. The system demonstrates potential for integration with existing surveillance infrastructure to expedite emergency response times. This research contributes to the growing field of acoustic event detection with specific applications in public safety, crime prevention, and smart city initiatives. The findings suggest that automated scream detection systems can serve as a valuable supplementary tool for law enforcement agencies to monitor high-risk areas and respond more efficiently to potential criminal activities.