@article {King:2018:0736-2935:1755,
title = "Emergency Vehicle Detection Using Acoustic Source Localization Techniques",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2018",
volume = "258",
number = "6",
publication date ="2018-12-18T00:00:00",
pages = "1755-1760",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2018/00000258/00000006/art00080",
author = "King, Eoin and Lagler, Jarrett B. and Tatoglu, Akin",
abstract = "Everyday, drivers use and process acoustic alerts when driving; examples include the emergency siren from an ambulance or a fire truck, or the sounding of a horn. These sounds alert the driver to an emergency vehicle in their vicinity, or an impending collision. However, as driver preference
is leading to increased acoustic isolation from their surroundings, it has become necessary to develop a platform where these acoustic alerts can still be delivered to a driver via other means. This paper presents results from an ongoing project that is developing an on-board system for the
rapid detection of emergency vehicle systems using acoustic source localization techniques. We are developing a platform that can automatically detect the presence of an emergency siren and the direction from which it is approaching. This will be useful to both human drivers enjoying increasingly
quieter cars, as well as future autonomous vehicles. Our system uses a multichannel cross-correlation algorithm for the estimation of the direction-of-arrival of a known acoustic source. We report results in both controlled and uncontrolled acoustic environments.",
}