Smart 311 Request System with Automatic Noise Detection for Safe Neighborhood

摘要

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, Naïve 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.