Enhancing Traffic Signals Awareness Through Sound Analysis for Ambulance Detection (KEY IJP************094)

• Malathi M,Aafaq Ahmed Namazi,Adnan Faseeh M S,Vineeth J

Abstract

The project aims to develop a robust real-time system utilizing TensorFlow, a prominent machine learning framework, to discern ambulance sounds amidst various environmental noises. Leveraging advanced machine learning techniques, the system will be trained to classify audio samples, specifically identifying the distinct siren patterns characteristic of ambulances, even in the presence of significant background noise. By harnessing the power of neural networks and deep learning algorithms, the objective is to create a highly dependable tool capable of accurately recognizing ambulance sounds in diverse real-world scenarios. Such a system holds immense potential for enhancing emergency response efforts by enabling swift detection and localization of ambulances, thereby facilitating quicker dispatch and route optimization. Additionally, the implementation of this technology could significantly contribute to bolstering public safety measures by minimizing response times during critical situations, ultimately saving lives and mitigating potential risks. This interdisciplinary endeavor merges cutting-edge technology with the pressing need for innovative solutions in emergency services, embodying the transformative potential of machine learning applications in addressing real-world challenges.