Abstract
Concerned about the noise pollution in urban environments, the European Commission (EC) has created an Environmental Noise Directive 2002/49/EC (END) requiring Member states to publish noise maps and noise management plans every five years for cities with a high density of inhabitants, major roads, railways and airports. The END also requires the noise pressure levels for these sources to be presented independently. Currently, data measurements and the representations of the noise pressure levels in such maps are performed semi-manually by experts. This process is time and cost consuming, as well as limited to presenting only a static picture of the noise levels. To overcome these issues, we propose the deployment of Wireless Acoustic Sensor Networks with several nodes in urban environments that can enable the generation of real-time noise level maps, as well as detect the source of the sound thanks to machine learning algorithms. In this paper, we briefly review the state of the art of the hardware used in wireless acoustic applications and propose a low-cost sensor based on an ARM cortex-A microprocessor. This node is able to process machine learning algorithms for sound source detection in-situ, allowing the deployment of highly scalable sound identification systems.
Highlights
The number of people living in urban areas has been greater than in rural areas since 2010; with around 50.5% of the world’s population residing in towns or cities [1]
This paper describes the design of a configurable low-cost acoustic sensor to enable the creation of Smart Wireless Acoustic Sensor Networks (WASNs) and the rapid development of sound identification solutions that take advantage of these networks
We reduced the requirements of the sensor because, on the one hand, it made the sensor cheaper, and on the other hand, the fact that the sound pressure level is usually measured in octaves, it works in the range between 31.5 Hz and 16,000 Hz, and the tails of this range are highly attenuated by the
Summary
The number of people living in urban areas has been greater than in rural areas since 2010; with around 50.5% of the world’s population residing in towns or cities [1]. The later is justified by large-scale studies in Europe that revealed severe adverse effects on the health and the life expectancy of the inhabitants of acoustically polluted environments [7,8]. We review noise monitoring projects that do not take into account the identification of the sound source. These projects are relevant because most of them use low-cost sensor networks, close to the work we present in this paper. (ii) capable of running machine learning algorithms in-situ for real-time operations within each sensor, and (iii) configurable to allow the rapid-development and evolution of sound monitoring solutions
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