Real-time Emergency Vehicle Event Detection Using Audio Data

TL;DR

This paper presents a real-time emergency vehicle detection system using audio data and extreme learning machines, achieving high accuracy with fast training suitable for online applications.

Contribution

The study introduces a simple, fast, and effective audio-based emergency vehicle detection method using ELMs, emphasizing real-time applicability.

Findings

Achieved 97% accuracy in emergency vehicle detection

ELMs provide comparable performance with shorter training times

Suitable for online, real-time emergency response systems

Abstract

In this work, we focus on detecting emergency vehicles using only audio data. Improved and quick detection can help in faster preemption of these vehicles at signalized intersections thereby reducing overall response time in case of emergencies. Important audio features were extracted from raw data and passed into extreme learning machines (ELM) for training. ELMs have been used in this work because of its simplicity and shorter run-time which can therefore be used for online learning. Recently, there have been many studies that focus on sound classification but most of the methods used are complex to train and implement. The results from this paper show that ELM can achieve similar performance with exceptionally shorter training times. The accuracy reported for ELM is about 97% for emergency vehicle detection (EVD).