Live Event Detection for People's Safety using NLP and Deep Learning

• Published in: IEEE access 2024-01, Vol.12, p.1-1
• Main Authors: Sen, Amrit, Rajakumaran, Gayathri, Mahdal, Miroslav, Usharani, Shola, Rajasekharan, Vezhavendhan, Vincent, Rajiv, Sugavanan, Karthikeyan
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Audio; Audio data; Audio signals; Cameras; Classification; Classification algorithms; CNN; Computer science; Deep learning; Emergency services; LSTM; Machine learning; Natural language processing; Natural language processing (NLP); NLP (Natural Language Processing); Prediction; Predictive models; Recording; Smart phones; Social networking (online)
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3349097

Today, humans pose the greatest threat to society by getting involved in robbery, assault, or homicide activities. Such circumstances threaten the people working alone at night in remote areas especially women. Any such kind of threat in real time is always associated with a sound/noise which may be used for an early detection. Numerous existing measures are available but none of them sounds efficient due to lack of accuracy, delays in exact prediction of threat. Hence a novel software-based prototype is developed to detect threats from a person's surrounding sound/noise and automatically alert the registered contacts of victims by sending email, SMS, WhatsApp messages through their smartphones without any other hardware components. Audio signals from Kaggle dataset are visualized, analyzed using Exploratory Data Analytics (EDA) techniques. By feeding EDA outcomes into various Deep Learning models: Long short-term memory (LSTM), Convolutional Neural Networks (CNN) yields accuracy of 96.6% in classifying the audio-events.