A safety-oriented framework for sound event detection in driving scenarios

The safety of drivers has become increasingly important in today's rapidly evolving transportation landscape, especially with the rise of autonomous and smart vehicles. This paper proposes a safety-oriented framework for sound event detection in smart vehicles using deep learning models. The goal of this framework is to increase driver awareness, prevent accidents, and provide acoustic forensic analysis. To achieve this, a meaningful taxonomy of event classes in a driving scenario is introduced, taking into account the event classes that are known to be related to major driving distractors. Based on this taxonomy, a dataset has been created to train and evaluate a fully-convolutional sound event detection model that was inspired by the well-known YOLO vision model. Experimental results demonstrated that the proposed model offers competitive results, outperforming a state-of-the-art baseline using recurrent connections. This comprehensive framework for sound event detection in smart vehicles aligns with the recommended directions for future mobility scenarios and has the potential to significantly improve the safety and performance of smart vehicles.