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Choosing Bootstrap Method for the Estimation of the Uncertainty of Traffic Noise Measurements
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. Univ. of Salerno.
Univ. of Salerno.
Univ. of Salerno.
Univ. of Salerno.
2017 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 66, no 5, p. 869-878Article in journal (Refereed) Published
Abstract [en]

The environmental acoustic noise is considered as a big risk for today's population health. Consequently, the regulations in many countries commit themselves to control the exposition of people, imposing limits to the noise level. In the comparison between the measured value and the threshold, the uncertainty of the measured value has to be taken into account. In this paper, a procedure for the evaluation of the uncertainty of traffic noise measurements due to the variability of the measurand is proposed. A deep analysis of five bootstrap (normal, basic percentile, t-student, bias corrected percentile, and bias corrected and accelerated percentile) methods is performed to obtain accurate confidence intervals for the indicator L<sub>eq</sub>.Awithout necessity to make normal theory assumptions. From the comparison with the classical method (according to Guide to the Expression of Uncertainty in Measurement (ISO GUM)), the novel approach reveals to be more effective for estimating both the expected value and the uncertainty of the short-term equivalent sound pressure level when a large data set is not available.

Place, publisher, year, edition, pages
2017. Vol. 66, no 5, p. 869-878
Keyword [en]
atmospheric techniques, noise (working environment), bias accelerated percentile, bias corrected percentile, bootstrap method, environmental acoustic noise, noise level limits, population health, short-term equivalent sound pressure level, traffic noise measurements, uncertainty estimation, Acoustic measurements, Acoustic noise, Estimation, Measurement uncertainty, Noise measurement, Standards, Uncertainty, statistical analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-33334DOI: 10.1109/TIM.2016.2627260ISI: 000399246000003Scopus ID: 2-s2.0-85018617621OAI: oai:DiVA.org:miun-33334DiVA, id: diva2:1192292
Available from: 2018-03-22 Created: 2018-03-22 Last updated: 2018-03-22Bibliographically approved

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