Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios

Muhammad Usaid; Muhammad Asif; Tabarka Rajab; Munaf Rashid; Syeda Iqra Hassan

doi:10.33317/ssurj.467

Authors

Muhammad Usaid Data Acquisition, Processing & Predictive Analytics Lab, NCBC
Muhammad Asif
Tabarka Rajab
Munaf Rashid
Syeda Iqra Hassan

DOI:

https://doi.org/10.33317/ssurj.467

Keywords:

Artificial Intelligence, Acoustic Monitoring, Deep Learning, Emergency Vehicle Siren, Multilayer Perceptron,

Abstract

Traffic density is growing day by day due to the increasing population and affordable prices of cars. It created a void for traffic management systems to cope with traffic congestion and prioritize ambulances. The consequences can be a terrible situation. Emergency vehicles are the most affected in these situations, and inadequate traffic control can put many lives at stake. Ambulances on the road are detected using an acoustic-based Artificial Intelligence system in this article. Emergency vehicle siren and road noise datasets have been developed for ambulance acoustic monitoring. The dataset is developed along with a deep learning (MLP-based) model and trained to use audio monitoring to predict the ambulance presence on the roads. This model achieved 90% accuracy when trained and validated against a developed dataset of only 300 files. With this validated algorithm, researchers can develop a real-time hardware-based model to detect emergency vehicles and make them arrive at the hospital as soon as possible.

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