GripNews➤ 聲音數據的分類方法：Wigner-Wille 分配與卷積神經網絡的應用
✤ https://arxiv.org/abs/2211.03202
這篇論文探討了在都市環境中利用聲音感應器數據的潛力。技術上，我們提出了一種使用 Wigner-Ville 分配和卷積神經網路對聲音數據進行分類的新方法。該研究基於作者的博士論文，在與荷蘭國家警察合作的愛因霍芬大學資料科學工程博士課程中進行。
+ 這項研究很有趣，可以為都市環境中的安全系統提供更好的警報功能。
+ 想知道這個方法是否在實際應用中有較高的成功率。
#音頻分類 #Wigner-Wille 分配 #卷積神經網路
"Seeing Sound": Audio Classification with the Wigner-Wille Distribution and Convolutional Neural Networks
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.
