Towards a system for automatic traffic sound event detection

Intelligent Traffic Surveillance systems have helped improve road safety through ensuring timely response to events such as traffic accidents and congestion. Our aim is to devise a robust system capable of traffic audio events detection in a real-life environment. At the core of this system is a deep learning model capable of detecting anomalous events and their classification based on their acoustic waveform. We present the results of a series of experiments designed to optimize the architecture of this model based on different algorithms for audio processing. The results show that the designed model has competitive performance to approaches published in literature.