Detection of Dangerous Situations by Sounds in Real-Time Using Deep Learning

This research paper presents a comprehensive exploration of the utilization of advanced machine learning techniques, specifically the Bidirectional Long Short-Term Memory (BiLSTM) neural network model, for the classification of dangerous urban sounds. In the context of increasingly complex urban environments, where a multitude of acoustic signals coexist, accurate identification of hazardous auditory cues is imperative for bolstering urban safety and security. The BiLSTM model emerges as a pivotal tool, adept at capturing intricate temporal relationships and contextual nuances, thereby facilitating the discrimination between benign and perilous urban sounds. Through rigorous experimentation, our model achieves a commendable 90% accuracy rate, signifying its potential for practical deployment in urban settings. Beyond model development, this research extends its impact by introducing a web application that enables real-time sound analysis and user engagement, bridging the gap between sophisticated machine learning capabilities and practical urban safety applications. As urban centers continue to evolve and confront the challenges posed by increasing population density and complexity, the fusion of advanced machine learning with the urban soundscape holds immense promise in the creation of safer and smarter urban ecosystems, ultimately enhancing the well-being and security of urban dwellers worldwide.