Wireless Sensor Networks for Noise Measurement and Acoustic Event Recognitions in Urban Environments

• Main Authors: Liyan Luo, Hongming Qin, Xiyu Song, Mei Wang, Hongbing Qiu, Zou Zhou
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Sensors
• Subjects: WASNs; noise measurement; acoustic events recognition; CNNs; real-life scenarios
• Online Access: https://www.mdpi.com/1424-8220/20/7/2093

Nowadays, urban noise emerges as a distinct threat to people’s physiological and psychological health. Previous works mainly focus on the measurement and mapping of the noise by using Wireless Acoustic Sensor Networks (WASNs) and further propose some methods that can effectively reduce the noise pollution in urban environments. In addition, the research on the combination of environmental noise measurement and acoustic events recognition are rapidly progressing. In a real-life application, there still exists the challenges on the hardware design with enough computational capacity, the reduction of data amount with a reasonable method, the acoustic recognition with CNNs, and the deployment for the long-term outdoor monitoring. In this paper, we develop a novel system that utilizes the WASNs to monitor the urban noise and recognize acoustic events with a high performance. Specifically, the proposed system mainly includes the following three stages: (1) We used multiple sensor nodes that are equipped with various hardware devices and performed with assorted signal processing methods to capture noise levels and audio data; (2) the Convolutional Neural Networks (CNNs) take such captured data as inputs and classify them into different labels such as car horn, shout, crash, explosion; (3) we design a monitoring platform to visualize noise maps, acoustic event information, and noise statistics. Most importantly, we consider how to design effective sensor nodes in terms of cost, data transmission, and outdoor deployment. Experimental results demonstrate that the proposed system can measure the urban noise and recognize acoustic events with a high performance in real-life scenarios.