论文标题
使用音频数据实时紧急车辆事件检测
Real-time Emergency Vehicle Event Detection Using Audio Data
论文作者
论文摘要
在这项工作中，我们专注于仅使用音频数据检测应急车辆。改进和快速检测可以有助于更快地在信号交叉点上对这些车辆进行预先抢先，从而在紧急情况下减少了总体响应时间。从原始数据中提取了重要的音频功能，并将其传递到极端的学习机器（ELM）进行培训。由于其简单性和较短的运行时间，因此在这项工作中使用了榆树，因此可以用于在线学习。最近，有许多研究专注于声音分类，但是大多数使用的方法是复杂的训练和实施方法。本文的结果表明，通过较短的训练时间，ELM可以实现相似的性能。榆木报告的准确性约为97％的紧急车辆检测（EVD）。
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).
