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

Abstract

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.

Deep Learning‐Enabled MXene/PEDOT:PSS Acoustic Sensor for Speech Recognition and Skin‐Vibration Detection

“…It is not difficult to find that it is consistent with the change of the black curve in Figure 3b. All the values of in the range of 100 Hz to 400 Hz are higher than the results reported by previous works, [ 14,31,39–41 ] indicating that the proposed MPAS has better sensitivity. The inset picture in Figure 3b shows the detailed results of the values of the sensor from 50 Hz to 500 Hz.…”

“…As a new research direction of machine learning, deep learning can effectively extract features by learning the inherent rules and connections of the sample data to achieve a prediction of the results. [31,[39][40][41]43] It is one of the effective ways to achieve artificial intelligence. The combination of deep learning with other fields has been extensively studied for the advantages of high learning capacity, wide coverage, strong adaptability, and good anti-interference.…”

Deep Learning-Assisted Triboelectric Smart Mats for Personnel Comprehensive Monitoring toward Maritime Safety

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A cantilever-structure triboelectric nanogenerator for energy harvesting and defect detection via long short-term memory network

“…The experimental results show that the sensor is sensitive to small liquid leakage and the classification accuracy is over 90%. In addition, many sensors based on triboelectric nanogenerators were proposed, such as self-powered acceleration sensors, 28 , 29 , 30 , 31 self-powered flowmeters, 32 , 33 sound monitoring and identification sensors, 34 , 35 vibration frequency monitoring sensors, 36 , 37 etc.…”