International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 01 | Jan 2025
p-ISSN: 2395-0072
www.irjet.net
Real-time Distress Signal Recognition: Acoustic Monitoring for Crime Control 1 Sirisha R, 2 Dhanalakshmi S, 3 Chetna L Shapur, 4Prof. Swathi Shrikanth Achanur 1,2,3 UG student, Dept. of CSE-AIML, AMC Engineering College, KARNATAKA, INDIA.
4Assistant professor, Dept. of CSE-AIML, AMC Engineering College, KARNATAKA, INDIA
------------------------------------------------------------------------***-----------------------------------------------------------------------alert mechanisms establishes this system as a pioneering ABSTRACT - This research focuses on developing a
approach to crime prevention and emergency response.
"Real-Time Acoustic Monitoring System for Crime Prevention," leveraging machine learning to detect human screams and assess risk levels. By leveraging Mel Frequency Cepstral Coefficients (MFCCs) for feature extraction and employing Support Vector Machines (SVM) and Multilayer Perceptrons (MLP) for classification, this system generates high-risk or medium-risk alert messages based on surrounding conditions. Integrating Kivy for the user interface and AWS SNS for SMS notifications ensures accessibility and timely communication with law enforcement.
2. EXISTING SYSTEM Currently, most crime prevention systems rely on visual surveillance, such as CCTV cameras, which provide video footage for monitoring and analysis. While effective in certain scenarios, these systems have significant limitations. Visual surveillance fails in poor lighting conditions, blind spots, or areas where camera installation is impractical. Moreover, such systems often require human intervention to monitor footage, making them less effective in real-time emergency scenarios.
Key Words: Acoustic monitoring, Human scream detection, MFCCs, SVM, MLP, Machine learning, Realtime alerts, Crime prevention, Kivy, AWS SNS
Audio-based detection systems, on the other hand, are still in their infancy. Although some systems can detect generic sounds, they often struggle to differentiate between distress sounds like screams and background noise, leading to high false-positive rates. Furthermore, existing solutions rarely Incorporate sophisticated machine learning algorithms for accurate classification, limiting their ability to adapt to dynamic environments.
1. INTRODUCTION In modern urban landscapes, where noise pollution is abundant, identifying and responding to distress sounds such as human screams is both challenging and critical. Traditional surveillance systems often rely on visual inputs, which may fail in low-visibility environments or locations without proper camera coverage. Additionally, the sheer volume of data generated by such systems makes manual monitoring impractical. This creates a pressing need for intelligent, real-Systems capable of independently detecting and interpret audio signals indicative of distress.
A strong need exists for a reliable system that not only detects screams with high precision but also evaluates the urgency of the situation and ensures timely communication with law enforcement agencies. This gap in existing systems forms the foundation for this research.
3. PROBLEM STATEMENT
This research focuses on developing an audio-based crime prevention system designed to detect human screams in real-time using machine learning models. By combining advanced sound processing techniques with risk assessment algorithms, the system ensures timely and accurate responses to potential emergencies. The integration of user-friendly interfaces and automated alert mechanisms adds further value, making this solution a promising approach to enhancing public safety.
The inability of existing surveillance systems to detect and respond to audio-based distress signals, such as human screams, in real-time has left a critical gap in public safety measures. Visual systems, while effective in certain conditions, fail in low-visibility scenarios or unmonitored areas. Furthermore, traditional audio detection systems lack the precision to distinguish distress sounds from background noise, resulting in delayed or missed responses.
This research aims to close the divide between traditional visual surveillance systems and advanced audio-based crime detection, offering a robust and efficient solution for enhancing public safety in real-time. The integration of intelligent algorithms and real-time
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This research aims to address these limitations by developing an intelligent, real-time acoustic monitoring system capable of detecting human screams, assessing the level of risk, and generating timely alerts. The proposed system should integrate machine learning
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