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Smart 311 Request System with Automatic Noise Detection for Safe Neighborhood

机译:SMART 311请求系统,具有安全邻域的自动噪声检测

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The aim of the smart city is to provide the technological access for the automation of city. This paper examines an audio classification application based on Machine Learning for detecting urban noise that is one of the major problems in many cities nowadays. There is an urgent need to develop an automatic urban noise detection system in enforcing public security and safe neighborhood. The urban noise detection was conducted using various Machine Learning algorithms including Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), Support Vector Machines, Decision Tree, Random Forest, Na?ve Bayes with 311 dataset and urban noise dataset. Our experiments validated that CNN shows the best performance (98% accuracy) compared to other ML algorithms. The prototype of the proposed system, Smart311, was developed for automatic urban noise detection in smart cities. The noise detection was conducted on mobile devices and connected to a real-time complaint system by sending a 311 request automatically.
机译:智能城市的目的是提供城市自动化的技术访问。本文介绍了基于机器学习的音频分类应用,用于检测城市噪音,这是现在许多城市的主要问题之一。迫切需要在强制公共安全和安全社区开发自动城市噪声检测系统。使用各种机器学习算法进行城市噪声检测,包括卷积神经网络(CNN),长短期记忆(LSTM),支持向量机,决策树,随机林,Na ve贝雷斯,带有311个数据集和城市噪声数据集。我们的实验验证了与其他ML算法相比,CNN显示了最佳性能(98 %精度)。建议系统的原型,Smart311是开发用于智能城市的自动城市噪声检测。在移动设备上进行噪声检测,并通过自动发送311请求连接到实时投诉系统。

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