NoiseTube: Measuring and mapping noise pollution with mobile phones

Maisonneuve, Nicolas; Stevens, Matthias; Niessen, Maria E.; Steels, Luc

doi:10.1007/978-3-540-88351-7_16

Cited by 310 publications

(212 citation statements)

References 15 publications

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“…Also, during the last decade, various studies focused on characterizing the quality of sound environments directly from acoustics measurements [4], [10], [12], [16], [21], [22]. These approaches are encouraged by novel acoustic mapping techniques based on low-cost sensor networks, or participative measurements, which allow obtaining sound maps with a higher spatial resolution, while being sensitive to all kind of sound sources constituting the sound environment [23]- [26]. Studies linking perceptual tests to acoustical measurements showed for instance that better estimates of the pleasantness of sound environments are obtained if parameters describing the temporal variations of sound, the sound spectrum, or the contributions of specific sound sources, are introduced in the modeling [8], [10], [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Modeling Soundscape Pleasantness Using perceptual Assessments and Acoustic Measurements Along Paths in Urban Context

Aumond¹,

Can²,

Coensel³

et al. 2017

Acta Acustica united with Acustica

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Mapping the pleasantness of an urban environment is an alternative approach, closer to the city dweller's perception, than standardized sound levels cartography. This study reports on modeling pleasantness in urban context using perceptual assessments and sound measurements for specific locations during an urban walk. These assessments have been collected from four groups of approximately ten participants on 19 different assessment locations, along a 2,1 km-long path traveled in both directions. Simultaneously, ⅓ octave band sound levels and audio were recorded. Perceptual and physical models of pleasantness are proposed for specific locations based on multiple linear regressions. A multilevel analysis was performed, and it is shown that a perceptual model that includes perceived loudness joined to the perceived time of presence of traffic, voices and birds explains 90% of the pleasantness variance due to the sound environment variations. Physical models that include the original acoustic indicators that are most correlated with perceptual variables explain 85% of this variance. Thanks to these models, a unique averaged pleasantness value is defined for each assessment location from the perceptual or physical collected assessments. The Pearson's correlation coefficient between the averaged perceived pleasantness and the modeled values from perceptual assessment reaches r(19)=0.98, and r(19)=0.97, with the modeled values from physical measurements. These results make it possible to consider the use of this kind of models in a cartographic context. As the path was traveled in both directions, the presentation-order effect has also been assessed, and it has been found that path direction did not have a significant impact on the pleasantness assessment at specific locations, except when very strong sound environment changes occurred. Finally, the study gives some insights about the retrospective global pleasantness assessment for urban walks. For very short walks between two assessment locations, a recency effect is shown. Nevertheless, this effect doesn't seem to be significant when longer routes are assessed.

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Section: Introductionmentioning

confidence: 99%

Modeling Soundscape Pleasantness Using perceptual Assessments and Acoustic Measurements Along Paths in Urban Context

Aumond¹,

Can²,

Coensel³

et al. 2017

Acta Acustica united with Acustica

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“…They have been widely used in mobile and ubiquitous computing, including location systems [27], measurement [18] and contextsensing [28]. Additional efforts have focussed on understanding and optimising the platform itself, considering the measurement of energy use [23], computation offload [7], or resource sharing between devices [25].…”

Section: Introductionmentioning

confidence: 99%

Device Analyzer: Understanding Smartphone Usage

Wagner

Rice

Beresford

2014

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. We describe Device Analyzer, a robust data collection tool which is able to reliably collect information on Android smartphone usage from an open community of contributors. We collected the largest, most detailed dataset of Android phone use publicly available to date. In this paper we systematically evaluate smartphones as a platform for mobile ubiquitous computing by quantifying access to critical resources in the wild. Our analysis of the dataset demonstrates considerable diversity in behaviour between users but also over time. We further demonstrate the value of handset-centric data collection by presenting case-study analyses of human mobility, interaction patterns, and energy management and identify notable differences between our results and those found by other studies.

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“…in order to plan routes and subsequently compare them by evaluating different available sensor data, such as noise, air pollution and so on. NoiseTube [85] uses this initiative to gather data from citizens' phones.…”

Section: Research Challenges Existing Giscience Contributions To Tackmentioning

confidence: 99%

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

Degbelo

Granell

Trilles

et al. 2016

IJGI

126

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Abstract:The holy grail of smart cities is an integrated, sustainable approach to improve the efficiency of the city's operations and the quality of life of citizens. At the heart of this vision is the citizen, who is the primary beneficiary of smart city initiatives, either directly or indirectly. Despite the recent surge of research and smart cities initiatives in practice, there are still a number of challenges to overcome in realizing this vision. This position paper points out six citizen-related challenges: the engagement of citizens, the improvement of citizens' data literacy, the pairing of quantitative and qualitative data, the need for open standards, the development of personal services, and the development of persuasive interfaces. The article furthermore advocates the use of methods and techniques from GIScience to tackle these challenges, and presents the concept of an Open City Toolkit as a way of transferring insights and solutions from GIScience to smart cities.

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NoiseTube: Measuring and mapping noise pollution with mobile phones

Cited by 310 publications

References 15 publications

Modeling Soundscape Pleasantness Using perceptual Assessments and Acoustic Measurements Along Paths in Urban Context

Modeling Soundscape Pleasantness Using perceptual Assessments and Acoustic Measurements Along Paths in Urban Context

Device Analyzer: Understanding Smartphone Usage

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

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