Laure-Anne Gille

Testing of the European Union exposure-response relationships and annoyance equivalents model for annoyance due to transportation noises: The need of revised exposure-response relationships and annoyance equivalents model

An in situ survey was performed in 8 French cities in 2012 to study the annoyance due to combined transportation noises. As the European Commission recommends to use the exposure-response relationships suggested by Miedema and Oudshoorn [Environmental Health Perspective, 2001] to predict annoyance due to single transportation noise, these exposure-response relationships were tested using the annoyance due to each transportation noise measured during the French survey. These relationships only enabled a good prediction in terms of the percentages of people highly annoyed by road traffic noise. For the percentages of people annoyed and a little annoyed by road traffic noise, the quality of prediction is weak. For aircraft and railway noises, prediction of annoyance is not satisfactory either. As a consequence, the annoyance equivalents model of Miedema [The Journal of the Acoustical Society of America, 2004], based on these exposure-response relationships did not enable a good prediction of annoyance due to combined transportation noises. Local exposure-response relationships were derived, following the whole computation suggested by Miedema and Oudshoorn [Environmental Health Perspective, 2001]. They led to a better calculation of annoyance due to each transportation noise in the French cities. A new version of the annoyance equivalents model was proposed using these new exposure-response relationships. This model enabled a better prediction of the total annoyance due to the combined transportation noises. These results encourage therefore to improve the annoyance prediction for noise in isolation with local or revised exposure-response relationships, which will also contribute to improve annoyance modeling for combined noises. With this aim in mind, a methodology is proposed to consider noise sensitivity in exposure-response relationships and in the annoyance equivalents model. The results showed that taking into account such variable did not enable to enhance both exposure-response relationships and the annoyance equivalents model.

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

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.

Dose-effect relationships for annoyance due to road traffic noise: Multi-level regression and consideration of noise sensitivity

An in situ survey was performed in eight French cities in 2012 to study the annoyance due to combined transportation noise sources. The European Union dose-effect relationships were compared to these new survey data for noise annoyance due to road traffic noise. The measured annoyance was not satisfactorily predicted by these curves: only the percentages of people highly annoyed by road traffic noise was well predicted. Following a multi-level regression as used to construct the European Union dose-effect relationships, new dose-effect relationships were proposed. These new dose-effect relationships enabled a better calculation of noise annoyance due to road traffic noise. Finally, a methodology to consider noise sensitivity in the computation and the percentage of people sensitive to noise in the results is proposed, as this non-acoustical factor is well known to influence noise annoyance. However, the results showed that taking into account such variable did not enable to enhance the dose-effect relatio...

Multilevel analysis of recent noise social survey data including noise sensitivity

Noise social surveys are typified by large variation in annoyance ratings for equivalent noise exposure. Some of this variation has previously been linked to individual attitudes, including noise sensitivity. A framework provided by multilevel statistical analysis can be used to study the relationships between noise annoyance and individual attitudes. The current effort applies multilevel analysis to two recent community surveys of impulsive noise for which noise-sensitivity was assessed via a baseline survey. In the results, the sizable annoyance variation among respondents is quantified. Multilevel models with random effect terms for slope and or intercept describe more variation than multiple regression models with only fixed effects for noise exposure and noise sensitivity.Noise social surveys are typified by large variation in annoyance ratings for equivalent noise exposure. Some of this variation has previously been linked to individual attitudes, including noise sensitivity. A framework provided by multilevel statistical analysis can be used to study the relationships between noise annoyance and individual attitudes. The current effort applies multilevel analysis to two recent community surveys of impulsive noise for which noise-sensitivity was assessed via a baseline survey. In the results, the sizable annoyance variation among respondents is quantified. Multilevel models with random effect terms for slope and or intercept describe more variation than multiple regression models with only fixed effects for noise exposure and noise sensitivity.

Dose-response model comparison of recent sonic boom community annoyance data

To enable quiet supersonic passenger flight overland, NASA is providing national and international noise regulators with a low-noise sonic boom database. The database will consist of dose-response curves, which quantify the relationship between low-noise sonic boom exposure and community annoyance. The recently-updated international standard for environmental noise assessment, ISO 1996-1:2016, references two fitting methods for dose-response analysis. Fidell’s community tolerance method is based on theoretical assumptions that fix the slope of the curve, allowing only the intercept to vary. By contrast, Miedema and Oudshoorn’s method is based on multilevel grouped regression. These fitting methods are applied to an existing pilot sonic boom community annoyance data set from 2011 with a small sample size. The purpose of this exercise is to develop data collection and analysis recommendations for future sonic boom community annoyance surveys.

Selection of a sound propagation model for noise annoyance prediction: A perceptual approach

Sound propagation effects need to be considered in studies dealing with the perception of annoying auditory sensations evoked by transportation noise. Thus, in a listening test requiring participants to make dissimilarity ratings, the effects of several feasible propagation models are compared to actual recordings of vehicle noises made at a given distance. As a result, a model taking into account first order reflections without any phase term is found to be the most appropriate model for simulating road traffic noise propagation in an urban environment from a perceptual point of view.

Testing of urban road traffic noise annoyance models—based on psychoacoustic indices—using in situ socio-acoustic survey

Noise annoyance is one of the main non-acoustical effects of noise. To manage this environmental issue, European cities of more than 100,000 inhabitants produce strategic noise maps which represent the noise exposure in terms of Lden. Despite this index is used in dose-effect relationships, several studies showed that models based on Lden insufficiently predict annoyance measured during in situ survey. Indeed, several acoustical characteristics (e.g. temporal variation) influence noise annoyance and are not considered by Lden. Laboratory experiments enable to propose annoyance models based on noise sensitivity and several psychoacoustic indices. These indices enable to characterize different acoustical characteristics. However, they cannot be measured for each respondent during an in situ survey. It was therefore not possible to test these annoyance models using in situ survey data. Thus, a methodology is proposed to estimate the values of the psychoacoustic indices on the basis of Lden given in survey da...

Structural equation modeling of partial and total annoyances due to urban road traffic and aircraft noises

Data of a French in situ socio-acoustic survey were used to model partial annoyance due to urban road traffic noise, partial annoyance due to aircraft noise and total annoyance due to these combined noises. Structural equation modeling carried out on the in situ data showed that long-term noise annoyance depends on noise exposure but also on noise disturbance, dwelling satisfaction, visibility of a main road from the dwelling and noise sensitivity. Both noise exposure and noise sensitivity were introduced as independent variables in structural equation modeling of partial and total noise annoyances. Their contributions to the models highlight the necessity to consider these two variables in annoyance model prediction. Finally, in total noise annoyance models, whereas partial annoyance due to aircraft noise contributes to total noise annoyance as much as partial road traffic annoyance, aircraft noise exposure contributes to total noise annoyance much more than road traffic noise. Several reasons may explai...

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

Structural equation modeling was used to analyze partial and total in situ annoyance in combined transportation noise situations. A psychophysical total annoyance model and a perceptual total annoyance model were proposed. Results show a high contribution of Noise exposure and Noise sensitivity to Noise annoyance, as well as a causal relationship between noise annoyance and lower Dwelling satisfaction. Moreover, the Visibility of noise source may increase noise annoyance, even when the visible noise source is different from the annoying source under study. With regards to total annoyance due to road traffic noise combined with railway or aircraft noise, even though in both situations road traffic noise may be considered background noise and the other noise source event noise, the contribution of road traffic noise to the models is greater than railway noise and smaller than aircraft noise. This finding may be explained by the difference in sound pressure levels between these two types of combined exposures or by the aircraft noise level, which may also indicate the city in which the respondents live. Finally, the results highlight the importance of sample size and variable distribution in the database, as different results can be observed depending on the sample or variables considered.

Summary of “Statistical learning and data science techniques in acoustics research”

Increases in computational capabilities have made statistical learning and data science techniques more accessible to researchers. This paper summarizes research presented in two special sessions, one each at the Spring 2016 meeting in Salt Lake City, UT and the Spring 2017 meeting in Boston, MA. The sessions were cosponsored by the Noise and Signal Processing Technical Committees. The speakers represent industry, academia, and government institutions in the United States, France, England, and the Netherlands. Presentation topics covered a variety of acoustic disciplines with a focus on machine learning techniques, Bayesian techniques, and advanced statistical models.