Real-Time Emergency Vehicle Detection using Mel Spectrograms and Regular Expressions
Abstract
In emergency situations, the high-speed movement of an ambulance through the city streets can be hindered by vehicular traffic. This work presents a method for detecting emergency vehicle sirens in real time. To obtain the audio fingerprint of a Hi-Lo siren, DSP and signal symbolization techniques were applied, which were contrasted against an audio classifier based on a deep neural network, using the same 280 audios of ambient sounds and 52 Hi-Lo siren audios dataset. In both methods, some classification accuracy metrics were evaluated based on its confusion matrix, resulting in the DSP algorithm having a slightly lower accuracy than the DNN model, however, it offers a self-explanatory, adjustable, portable, high performance and lower energy and consumption that makes it a more viable lower cost ADAS implementation to identify Hi-Lo sirens in real time.
Cite
@article{arxiv.2309.13920,
title = {Real-Time Emergency Vehicle Detection using Mel Spectrograms and Regular Expressions},
author = {Alberto Pacheco-Gonzalez and Raymundo Torres and Raul Chacon and Isidro Robledo},
journal= {arXiv preprint arXiv:2309.13920},
year = {2024}
}
Comments
in Spanish language