Estimation of the minimum measurement time interval in acoustic noise

Abstract The appropriate choice of the minimum measurement time interval is introduced for an accurate estimation of environmental noise indicators. The proposal is based on a bootstrap approach for the continuous estimation of measurement uncertainty in order to determine the statistical variability of the acquired sound pressure levels. Experimental results concerning the adoption of the proposed method regarding environmental noise from three different sources (road traffic, outdoor air conditioner fan motor and construction site) confirm the reliability of the proposal and its feasibility in evaluating the equivalent sound pressure level of an acoustic phenomenon using short-term indicators.

[1]  Antonio Pietrosanto,et al.  A Statistical Approach for Improving the Performance of a Testing Methodology for Measurement Software , 2008, IEEE Transactions on Instrumentation and Measurement.

[2]  Shiu-keung Tang,et al.  On monitoring community noise using arbitrarily chosen measurement periods , 2008 .

[3]  Alan M. Polansky,et al.  Stabilizing bootstrap‐t confidence intervals for small samples , 2000 .

[4]  Domenico Russo,et al.  Acoustical design and experimental verification of school music rooms: A case study , 2016 .

[5]  David P Mackinnon,et al.  Resampling and Distribution of the Product Methods for Testing Indirect Effects in Complex Models , 2008, Structural equation modeling : a multidisciplinary journal.

[6]  Mirko Čudina,et al.  Determination of the sound energy level of a gunshot and its applications in room acoustics , 2016 .

[7]  Dietrich Heimann,et al.  Derivation of sound-level characteristics to assess traffic development scenarios , 2016 .

[8]  Madjid Abbaspour,et al.  An Investigation on Time-Interval Optimisation of Traffic Noise Measurement , 2006 .

[9]  S. R. De Donato.  Estimation of the minimum integration time for determining the equivalent continuous sound level with a given level of uncertainty considering some statistical hypotheses for road traffic , 2007 .

[10]  Gilles Fleury,et al.  Bootstrap methods for a measurement estimation problem , 2006, IEEE Transactions on Instrumentation and Measurement.

[11]  R. A. Pyron,et al.  The taming of the skew: estimating proper confidence intervals for divergence dates. , 2008, Systematic biology.

[12]  Mitsuo Ohta,et al.  Time series prediction method for stochastic acoustic signals by the use of an adaptive function model , 1989 .

[13]  Bartłomiej Stępień.  Bootstrap Confidence Intervals for Noise Indicators , 2016 .

[14]  Dimitris Skarlatos,et al.  Noise probability density functions for poisson type traffic flow , 1989 .

[16]  Alessandro Ruggiero,et al.  Influence of measurement time on acoustic noise uncertainty , 2017, 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[17]  Adrià Giménez,et al.  Sampling techniques for the estimation of the annual equivalent noise level under urban traffic conditions , 2003 .

[18]  Application of the Bayesian Inference for Estimation of the Long-Term Noise Indicators and Their Uncertainty , 2011 .

[19]  Toshio Sone,et al.  The minimum measurement time for estimating LAeqT of road traffic noise from the number of vehicle pass-bys , 2013 .

[20]  Dimitris Skarlatos,et al.  On selecting the minimum observation time for determining the Leq of a random noise with a given level of confidence , 1992 .

[21]  Mirko Čudina,et al.  Estimating uncertainty of measurements for cavitation detection in a centrifugal pump , 2011 .

[22]  Alessandro Ruggiero,et al.  Innovative bootstrap approach for the estimation of minimum measurement time interval in road traffic noise evaluation , 2017 .

[23]  Alfredo Paolillo,et al.  Towards the evaluation of the measurement uncertainty of environmental acoustic noise , 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[24]  W. Au,et al.  Statistical structures of indoor traffic noise in a high-rise city , 1999 .

[25]  Wojciech Batko,et al.  Type a Standard Uncertainty of Long-Term Noise Indicators , 2015 .

[26]  Aleksandras Jagniatinskis,et al.  Assessment of environmental noise from long-term window microphone measurements , 2014 .

[27]  Hein Putter,et al.  The bootstrap: a tutorial , 2000 .

[28]  Alfredo Paolillo,et al.  Outlier Detection for the Evaluation of the Measurement Uncertainty of Environmental Acoustic Noise , 2016, IEEE Transactions on Instrumentation and Measurement.

[29]  T. Sone,et al.  Minimum measurement time interval to estimate a reliable sound pressure level of road traffic noise using two types of dynamic statistics , 2014 .

[30]  A. Mohammad-Shahri,et al.  Uncertainty evaluation for an ultrasonic data fusion based target differentiation problem using Generalized Aggregated Uncertainty measure 2 , 2014 .

[31]  Alfredo Paolillo,et al.  A preliminary study on the estimation of the uncertainty of traffic noise measurements , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[32]  Dimitris Skarlatos,et al.  A NUMERICAL METHOD FOR CALCULATION OF PROBABILITY DENSITY FUNCTION OF EQUIVALENT LEVEL IN THE CASE OF TRAFFIC NOISE , 1993 .

[33]  Alessandro Ruggiero,et al.  Determining environmental noise measurement uncertainty in the context of the Italian legislative framework , 2016 .

[34]  Naveen Garg,et al.  Reaffirmation of measurement uncertainty in pressure sensitivity determination of LS2P microphones by reciprocity method , 2014 .

[35]  R. Tibshirani,et al.  An introduction to the bootstrap , 1993 .

[36]  Alessandro Ruggiero,et al.  Choosing Bootstrap Method for the Estimation of the Uncertainty of Traffic Noise Measurements , 2017, IEEE Transactions on Instrumentation and Measurement.

[37]  Anthony C. Davison,et al.  Bootstrap Methods and Their Application , 1998 .