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      A Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment

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          Abstract

          Noise is a major source of pollution with a strong impact on health. Noise assessment is therefore a very important issue to reduce its impact on humans. To overcome the limitations of the classical method of noise assessment (such as simulation tools or noise observatories), alternative approaches have been developed, among which is collaborative noise measurement via a smartphone. Following this approach, the NoiseCapture application was proposed, in an open science framework, providing free access to a considerable amount of information and offering interesting perspectives of spatial and temporal noise analysis for the scientific community. After more than 3 years of operation, the amount of collected data is considerable. Its exploitation for a sound environment analysis, however, requires one to consider the intrinsic limits of each collected information, defined, for example, by the very nature of the data, the measurement protocol, the technical performance of the smartphone, the absence of calibration, the presence of anomalies in the collected data, etc. The purpose of this article is thus to provide enough information, in terms of quality, consistency, and completeness of the data, so that everyone can exploit the database, in full control.

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          Most cited references70

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          A Survey of Online Activity Recognition Using Mobile Phones

          Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.
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            Evaluation of smartphone sound measurement applications.

            This study reports on the accuracy of smartphone sound measurement applications (apps) and whether they can be appropriately employed for occupational noise measurements. A representative sample of smartphones and tablets on various platforms were acquired, more than 130 iOS apps were evaluated but only 10 apps met our selection criteria. Only 4 out of 62 Android apps were tested. The results showed two apps with mean differences of 0.07 dB (unweighted) and -0.52 dB (A-weighted) from the reference values. Two other apps had mean differences within ±2 dB. The study suggests that certain apps may be appropriate for use in occupational noise measurements.
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              NoiseSPY: A Real-Time Mobile Phone Platform for Urban Noise Monitoring and Mapping

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                Author and article information

                Contributors
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                Journal
                IJERGQ
                International Journal of Environmental Research and Public Health
                IJERPH
                MDPI AG
                1660-4601
                August 2021
                July 22 2021
                : 18
                : 15
                : 7777
                Article
                10.3390/ijerph18157777
                297dff1f-f621-43d4-814d-29dc68416991
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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