  With big data becoming increasingly available, IoT hardware becoming widely
adopted, and AI capabilities becoming more powerful, organizations are
continuously investing in sensing. Data coming from sensor networks are
currently combined with sensor fusion and AI algorithms to drive innovation in
fields such as self-driving cars. Data from these sensors can be utilized in
numerous use cases, including alerts in safety systems of urban settings, for
events such as gun shots and explosions. Moreover, diverse types of sensors,
such as sound sensors, can be utilized in low-light conditions or at locations
where a camera is not available. This paper investigates the potential of the
utilization of sound-sensor data in an urban context. Technically, we propose a
novel approach of classifying sound data using the Wigner-Ville distribution
and Convolutional Neural Networks. In this paper, we report on the performance
of the approach on open-source datasets. The concept and work presented is
based on my doctoral thesis, which was performed as part of the Engineering
Doctorate program in Data Science at the University of Eindhoven, in
collaboration with the Dutch National Police. Additional work on real-world
datasets was performed during the thesis, which are not presented here due to
confidentiality.
