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Noise annoyance from a mixture of multiple single sources: rating and prediction

Yan Liang Chen Ke-An Ruedi Stoop

Noise annoyance from a mixture of multiple single sources: rating and prediction

Yan Liang, Chen Ke-An, Ruedi Stoop
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  • Abstract views:  1564
  • PDF Downloads:  409
  • Cited By: 0
Publishing process
  • Received Date:  21 October 2011
  • Accepted Date:  20 December 2011
  • Published Online:  20 August 2012

Noise annoyance from a mixture of multiple single sources: rating and prediction

  • 1. College of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
  • 2. Institute of Neuroinformatics, Swiss Federal Institute of Technology Zurich, Zurich 8057, Switzerland
Fund Project:  Project supported by the Foundation for Fundamental Research of Northwestern Polytechnical University, China (Grant No. JC201025).

Abstract: In this paper, noise annoyance from a mixture of multiple single sources is studied with emphasis on subjective evaluation and objective prediction. From 10 subjects, annoyance values for all single and artificially combined noise samples are collected using the semantic differential method with a suitable verbal scale. We propose a novel method to determine the utility weights of a multivariate linear regression model by comparing the total annoyance T of the combined noise sample to every single annoyance i from its componential single sound sample. This method predicts T on the premise of given i. Our results demonstrate that the multivariate linear regression model and the calculated utility weights provide a good and conceptually simple framework to predict the total noise annoyance.

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