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).
翻译:在这项工作中,我们的重点是只使用音频数据探测紧急车辆。改进和快速探测有助于在信号交叉点更快地预防这些车辆,从而减少紧急情况下的总体反应时间。重要的音频功能是从原始数据中提取出来的,并传递到极端学习机(ELM)用于培训。这项工作使用了ELM,因为其简单和较短的运行时间,因此可用于在线学习。最近,进行了许多研究,侧重于健全的分类,但使用的方法大多很复杂,需要培训和实施。本文的结果表明,ELM在培训时间极短的情况下可以取得类似的性能。报告的ELM的精确度约为97%,用于紧急车辆探测(EVD)。