IoT based Urban Noise Monitoring in Deep Learning using Historical Reports

In this paper, we propose a new Internet of things (IoT) solution, called the Urban Noise Monitoring (UNM) system, which can classify real-time environmental audio sound using an embedded system such as Raspberry pi 4 and log the data in the Google Cloud. The reported events will be available for future usage, i.e., selection of the safe area for living. The real-time audio classification has been a big challenge for deep learning in environmental sounds due to the high noisy nature of sound. We have implemented a real-time IoT system for urban sound classification and monitored the historical reports generated. We have developed an advanced fusion method using normalization techniques such as peak, RMS, and EBU and an efficient data augmentation method using various factors, including time stretch, pitch shifting, and dynamic range compression. Further, we have integrated the normalization and the augmentation methods into 2D Convolutional Neural Network (CNN) with the TensorFlow framework on Raspberry pi 4 for urban sound classification. Our classification model outperformed the state of the art performance: 95% accuracy with the Urban sound dataset. The outstanding performance confirmed the effectiveness of the proposed method on the IoT system for urban noise monitoring.