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With big data becoming increasingly available, IoT hardware becoming widelyadopted, and AI capabilities becoming more powerful, organizations arecontinuously investing in sensing. Data coming from sensor networks arecurrently combined with sensor fusion and AI algorithms to drive innovation infields such as self-driving cars. Data from these sensors can be utilized innumerous use cases, including alerts in safety systems of urban settings, forevents such as gun shots and explosions. Moreover, diverse types of sensors,such as sound sensors, can be utilized in low-light conditions or at locationswhere a camera is not available. This paper investigates the potential of theutilization of sound-sensor data in an urban context. Technically, we propose anovel approach of classifying sound data using the Wigner-Ville distributionand Convolutional Neural Networks. In this paper, we report on the performanceof the approach on open-source datasets. The concept and work presented isbased on my doctoral thesis, which was performed as part of the EngineeringDoctorate program in Data Science at the University of Eindhoven, incollaboration with the Dutch National Police. Additional work on real-worlddatasets was performed during the thesis, which are not presented here due toconfidentiality.

"Seeing Sound": Audio Classification with the Wigner-Wille Distribution and Convolutional Neural Networks