Abstract
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.
Highlights
In urban environments, human activities such as transportation [1], industrial manufacturing [2], and building construction [3] lead to an increase in noise pollution, which poses a distinct threat to people’s health [4] and quality of life [5]
Project [12], two Wireless Acoustic Sensor Networks (WASNs) have been deployed in two pilot areas to measure the noise levels detected by the sensors, which are generally used to scale basic noise maps stored in a database and processed on a general GIS platform
We evaluate the performance of the proposed system based on WASN that consists of 50 where Xis flattened to a column vector of length N, W has a shape (256,800), b is a vector of length M, sensor nodes, mainly related to the recognition and monitoring platform
Summary
Human activities such as transportation [1], industrial manufacturing [2], and building construction [3] lead to an increase in noise pollution, which poses a distinct threat to people’s health [4] and quality of life [5]. The combination of numerous smartphones acting as sound level meters allows achieving large-scale monitoring. Different algorithms such as Fourier-based algorithm, Sensors 2020, 20, 2093; doi:10.3390/s20072093 www.mdpi.com/journal/sensors. The well-known project, DYNAMAP [11], aiming to develop a dynamic noise mapping system can detect and represent the acoustic impact of road infrastructures using customized sensors and communication devices in real time. In the framework of the LIFE DYNAMAP project [12], two WASNs have been deployed in two pilot areas to measure the noise levels detected by the sensors, which are generally used to scale basic noise maps stored in a database and processed on a general GIS platform. We adopted binary classification methods such as fall detection [32], and the following three success criteria could be used to measure its performance
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