  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.
