Literature DB >> 28040048

Noise sensitivity and loudness derivative index for urban road traffic noise annoyance computation.

Laure-Anne Gille1, Catherine Marquis-Favre2, Reinhard Weber3.   

Abstract

Urban road traffic composed of powered-two-wheelers (PTWs), buses, heavy, and light vehicles is a major source of noise annoyance. In order to assess annoyance models considering different acoustical and non-acoustical factors, a laboratory experiment on short-term annoyance due to urban road traffic noise was conducted. At the end of the experiment, participants were asked to rate their noise sensitivity and to describe the noise sequences they heard. This verbalization task highlights that annoyance ratings are highly influenced by the presence of PTWs and by different acoustical features: noise intensity, irregular temporal amplitude variation, regular amplitude modulation, and spectral content. These features, except irregular temporal amplitude variation, are satisfactorily characterized by the loudness, the total energy of tonal components and the sputtering and nasal indices. Introduction of the temporal derivative of loudness allows successful modeling of perceived amplitude variations. Its contribution to the tested annoyance models is high and seems to be higher than the contribution of mean loudness index. A multilevel regression is performed to assess annoyance models using selected acoustical indices and noise sensitivity. Three models are found to be promising for further studies that aim to enhance current annoyance models.

Entities:  

Year:  2016        PMID: 28040048     DOI: 10.1121/1.4971329

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  2 in total

1.  Partial and Total Annoyance Due to Road Traffic Noise Combined with Aircraft or Railway Noise: Structural Equation Analysis.

Authors:  Laure-Anne Gille; Catherine Marquis-Favre; Kin-Che Lam
Journal:  Int J Environ Res Public Health       Date:  2017-11-30       Impact factor: 3.390

2.  Influencing Factors Identification and Prediction of Noise Annoyance-A Case Study on Substation Noise.

Authors:  Guoqing Di; Yihang Wang; Yao Yao; Jiangang Ma; Jian Wu
Journal:  Int J Environ Res Public Health       Date:  2022-07-09       Impact factor: 4.614

  2 in total

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