Real-Time Emergency Vehicle Detection using Mel Spectrograms and Regular Expressions

In emergency situations, the movement of vehicles through city streets can be problematic due to vehicular traffic. This paper presents a method for detecting emergency vehicle sirens in real time. To derive a siren Hi-Lo audio fingerprint it was necessary to apply digital signal processing techniques and signal symbolization, contrasting against a deep neural network audio classifier feeding 280 environmental sounds and 38 Hi-Lo sirens. In both methods, their precision was evaluated based on a confusion matrix and various metrics. The precision of the developed DSP algorithm presented a greater ability to discriminate between signal and noise, compared to the CNN model.