Abstract

Traffic congestion, accidents, and pollution are becoming a challenge for researchers. It is essential to develop new ideas to solve these problems, either by improving the infrastructure or applying the latest technology to use the existing infrastructure better. This research paper presents a high-resolution dataset that will help the research community to apply AI techniques to classify any emergency vehicle from traffic and road noises. Demand for such datasets is high as they can control traffic flow and reduce traffic congestion. It also improves emergency response time, especially for fire and health events. This work collects audio data using different methods, and pre-processed them  to develop a high-quality and clean dataset. The dataset is divided into two labelled classes one for emergency vehicle sirens and one for traffic noises. The developed dataset offers high quality and range of real-world traffic sounds and emergency vehicle sirens. The technical validity of the dataset is also established.

Measurement(s)

Ambulance Siren and Road Noises. (Recordings)

Technology Type(s)

Microphone

Sample Characteristic - Environment

Roads

Sample Characteristic - Location

Pakistan

Details

Title
Large-scale audio dataset for emergency vehicle sirens and road noises
Author
Asif, Muhammad 1 ; Usaid, Muhammad 1   VIAFID ORCID Logo  ; Rashid, Munaf 1 ; Rajab, Tabarka 1 ; Hussain, Samreen 2 ; Wasi, Sarwar 1 

1  Ziauddin University, Data Acquisition, Processing, and Predictive Analytics, NCBC, Karachi, Pakistan (GRID:grid.413093.c) (ISNI:0000 0004 0571 5371) 
2  Aror University of Art, Architecture, Design and Heritage, Sukker, Pakistan (GRID:grid.413093.c) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20524463
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41597-022-01727-2
ProQuest document ID
2721084709
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.