Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sir Syed University of Engineering and Technology, Karachi.
2022-06-01
|
| Series: | Sir Syed University Research Journal of Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/467 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849434275337732096 |
|---|---|
| author | Muhammad Usaid Muhammad Asif Tabarka Rajab Munaf Rashid Syeda Iqra Hassan |
| author_facet | Muhammad Usaid Muhammad Asif Tabarka Rajab Munaf Rashid Syeda Iqra Hassan |
| author_sort | Muhammad Usaid |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-7268ef4b70b34202b9cd997f638ccbb7 |
| institution | Kabale University |
| issn | 1997-0641 2415-2048 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Sir Syed University of Engineering and Technology, Karachi. |
| record_format | Article |
| series | Sir Syed University Research Journal of Engineering and Technology |
| spelling | doaj-art-7268ef4b70b34202b9cd997f638ccbb72025-08-20T03:26:43ZengSir Syed University of Engineering and Technology, Karachi.Sir Syed University Research Journal of Engineering and Technology1997-06412415-20482022-06-01121Ambulance Siren Detection using Artificial Intelligence in Urban ScenariosMuhammad Usaid0Muhammad AsifTabarka RajabMunaf RashidSyeda Iqra HassanData Acquisition, Processing & Predictive Analytics Lab, NCBC 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. http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/467Artificial Intelligence, Acoustic Monitoring, Deep Learning, Emergency Vehicle Siren, Multilayer Perceptron, |
| spellingShingle | Muhammad Usaid Muhammad Asif Tabarka Rajab Munaf Rashid Syeda Iqra Hassan Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios Sir Syed University Research Journal of Engineering and Technology Artificial Intelligence, Acoustic Monitoring, Deep Learning, Emergency Vehicle Siren, Multilayer Perceptron, |
| title | Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios |
| title_full | Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios |
| title_fullStr | Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios |
| title_full_unstemmed | Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios |
| title_short | Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios |
| title_sort | ambulance siren detection using artificial intelligence in urban scenarios |
| topic | Artificial Intelligence, Acoustic Monitoring, Deep Learning, Emergency Vehicle Siren, Multilayer Perceptron, |
| url | http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/467 |
| work_keys_str_mv | AT muhammadusaid ambulancesirendetectionusingartificialintelligenceinurbanscenarios AT muhammadasif ambulancesirendetectionusingartificialintelligenceinurbanscenarios AT tabarkarajab ambulancesirendetectionusingartificialintelligenceinurbanscenarios AT munafrashid ambulancesirendetectionusingartificialintelligenceinurbanscenarios AT syedaiqrahassan ambulancesirendetectionusingartificialintelligenceinurbanscenarios |