Intelligent Sound Monitoring and Identification System Combining Triboelectric Nanogenerator-Based Self-Powered Sensor with Deep Learning Technique

Urban sound management is required in a variety of fields such as transportation, security, water conservancy and construction, among others. Given the diverse array of available noise sensors and the widespread opportunity to connect these sensors via mobile broadband Internet access, many researchers are eager to apply sound-sensor networks for urban sound management. Existing sensing networks typically consist of expensive information-sensing devices, the cost and maintenance of which limit their large-scale, ubiquitous deployment, thus narrowing their functional measurement range. Herein, an innovative, low-cost, sound-driven triboelectric nanogenerator (SDTENG)-based self-powered sensor is proposed, from which the SDTENG is primarily comprised of fluorinated ethylene propylene membranes, conductive fabrics, acrylic shells, and Kapton spacers. The SDTENG-based sensor has been integrated with a deep learning technique in the present study to construct an intelligent sound monitoring and identification system, which is capable of recognizing a suite of common road and traffic sounds with high classification accuracies of 99% in most cases. The novel SDTENG-based self-powered sensor combined with deep learning technique demonstrates a tremendous application potential in urban sound management, which will show the excellent application prospects in the field of ubiquitous sensor networks.

Automated summary

In this paper, a novel low-cost self-powered sensor based on SDTENG is proposed, and it is integrated with deep learning technique to construct an intelligent sound monitoring and identification system. The system can recognize common road and traffic sounds with high accuracy, showing great potential in urban sound management.