Abstract
Nowadays, smartphones have become ubiquitous and one of the main communication resources for human beings. Their widespread adoption was due to the huge technological progress and to the development of multiple useful applications. Their characteristics have also experienced a substantial improvement as they now integrate multiple sensors able to convert the smartphone into a flexible and multi-purpose sensing unit. The combined use of multiple smartphones endowed with several types of sensors gives the possibility to monitor a certain area with fine spatial and temporal granularity, a procedure typically known as crowdsensing. In this paper, we propose using smartphones as environmental noise-sensing units. For this purpose, we focus our study on the sound capture and processing procedure, analyzing the impact of different noise calculation algorithms, as well as in determining their accuracy when compared to a professional noise measurement unit. We analyze different candidate algorithms using different types of smartphones, and we study the most adequate time period and sampling strategy to optimize the data-gathering process. In addition, we perform an experimental study comparing our approach with the results obtained using a professional device. Experimental results show that, if the smartphone application is well tuned, it is possible to measure noise levels with a accuracy degree comparable to professional devices for the entire dynamic range typically supported by microphones embedded in smartphones, i.e., 35–95 dB.
Highlights
Noise is considered a particular type of environmental pollutant since, at certain levels, it can affect people both physiologically and psychologically and, more important, interfere with basic activities, such as sleep, rest, study, communication and socializing [1,2]
The traditional approach to measuring acoustic pollution is through the use of professional sound level meters, which are of considerable cost and size, having high accuracy and sensitivity
It should be noted that the sampling rate and the block size affect the accuracy of noise calculations due to two factors: frequency resolution and filtering
Summary
Noise is considered a particular type of environmental pollutant since, at certain levels, it can affect people both physiologically and psychologically and, more important, interfere with basic activities, such as sleep, rest, study, communication and socializing [1,2]. Have highlighted the importance of noise control in highly-populated areas. The traditional approach to measuring acoustic pollution is through the use of professional sound level meters, which are of considerable cost and size, having high accuracy and sensitivity. These measurements are taken at a limited number of places and are processed using different statistical techniques to generate acoustic pollution maps for well-defined target areas, thereby providing finer spatial granularity. The basic concept behind crowdsensing is that all users contribute to the Sensors 2017, 17, 917; doi:10.3390/s17040917 www.mdpi.com/journal/sensors
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