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.
翻译:随着大数据日益普及,互联网硬件被广泛采用,AI能力越来越强大,各组织正在不断投资于遥感;来自传感器网络的数据目前与感应聚合和AI算法相结合,在诸如自行驾驶汽车等领域推动创新;这些传感器的数据可用于许多使用案例,包括城市安全系统、枪支射击和爆炸等事件的警报;此外,各种类型的传感器,例如声音传感器,可以在低光条件下或在没有相机的地点使用;本文调查在城市环境中利用声音传感器数据的可能性;技术上,我们提议采用新颖的方法,利用Wigner-Ville分布和动态神经网络对声音数据进行分类;在本文件中,我们报告公开源数据集做法的绩效;所提出的概念和工作以我的博士论文为基础,作为Eindhoven大学数据科学工程博士方案的一部分,与荷兰国家警察局合作,进行了数据学应用;在本文中,没有提交真实世界数据保密性的额外工作。