IoT smart city framework using AI for urban sound classification

Abstract

The advances of artificial intelligence approaches for automatically extracting and classifying disturbing sounds have great potential and application in the development of smart cities. In this paper, an urban sound event classification system based on deep learning technologies has been created by using the MEL-frequency cepstral coefficients as feature extractors and the Convolutional Neural Networks as classifiers. The designed system was trained and tested using the UrbanSound8K dataset which resulted in high classification accuracy of 92.67% of the tested results. In addition to this, validation of unknown sound events was applied. Furthermore, we propose the creation of a stand-alone system capable of recording and classifying sounds within an urban environment and sending the classification data to the cloud. The implementation of such units could result in real-time sound classification which can be used in smart cities based on the Internet of Things technology. According to this, an IoT smart city framework using AI for urban sound classification was proposed. The created system could find its use in many applications by making contribution in creating a feasible and deployable real-time sound classification system.