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.

翻译：在紧急情况下，救护车在城市街道中的高速通行可能受到车辆交通的阻碍。本研究提出一种实时检测紧急车辆警报声的方法。为获取Hi-Lo警报声的音频指纹，本研究应用了数字信号处理与信号符号化技术，并基于包含280段环境音与52段Hi-Lo警报声的相同数据集，与基于深度神经网络的音频分类器进行对比。两种方法均通过混淆矩阵评估了分类准确度指标，结果显示数字信号处理算法的准确度略低于深度神经网络模型，但其具备自解释性、可调节性、可移植性、高性能及低能耗等优势，这使其成为实时识别Hi-Lo警报声时更具可行性且成本更低的先进驾驶辅助系统实施方案。