Emergency Siren Detection is a topic of great importance for road safety. Nowadays, the design of cars with every comfort has improved the quality of driving, but distractions have also increased. Hence the usefulness of implementing an Emergency Vehicle Detection System: if installed inside the car, it alerts the driver of its approach, and if installed outdoors in strategic locations, it automatically activates reserved lanes. In this paper, we perform Emergency Siren Detection with a Convolutional Neural Network-based deep learning model. We investigate acoustic features to propose a low computational cost algorithm. We employ Short-Time Fourier Transform spectrograms as features and improve the classification performance by applying a harmonic percussive source separation technique. The enhancement of the harmonic components of the spectrograms gives better results than more computationally complex features. We also demonstrate the relevance of the siren harmonic contents in the classification task. The reduction of the network hyperparameters decreases the computational load of the algorithm and facilitates its implementation in real-time embedded systems.
Acoustic features for deep learning-based models for emergency siren detection: An evaluation study / Cantarini, M.; Brocanelli, A.; Gabrielli, L.; Squartini, S.. - In: ISPA. - ISSN 1845-5921. - ELETTRONICO. - 2021-:(2021), pp. 47-53. (Intervento presentato al convegno 12th International Symposium on Image and Signal Processing and Analysis, ISPA 2021 tenutosi a hrv nel 2021) [10.1109/ISPA52656.2021.9552140].