Annoyance and Partial Masking of Wind Turbine Noise from Ambient Sources

2019 ◽  
Vol 105 (6) ◽  
pp. 1035-1041 ◽  
Author(s):  
Anders Johansson ◽  
Karl Bolin ◽  
Jesper Alvarsson
Keyword(s):  
Wind Turbine ◽  
Road Traffic ◽  
Deciduous Forest ◽  
Traffic Noise ◽  
Repeated Measurements ◽  
Road Traffic Noise ◽  
Time Varying ◽  
Signal To Noise ◽  
Wind Turbine Noise ◽  
Sound Levels

This paper investigates noise annoyance from wind turbines of different sizes and in different acoustic surroundings. A listening test was conducted where wind turbine noises were rated alone and together with background sounds from a deciduous forest, a busy city and road traffic. A magnitude production procedure was implemented which showed high correlation between repeated measurements and the results were analysed using A-weighted sound levels, signal-to-noise ratios and time varying loudness and partial loudness. Ratings for wind turbine sound heard alone showed no coherent statistically significant differences between wind turbine types, neither for A-weighted sound levels nor loudness. The masking test indicate that road traffic noise is a superior masker compared to forest sound. However, these effects where only statistically significant at low sound levels, below the range 35–45 dB(A), where noise guidelines for wind turbine noise usually are stipulated.

scholarly journals Short-term annoyance reactions to stationary and time-varying wind turbine and road traffic noise: A laboratory study

2016 ◽  
Vol 139 (5) ◽  
pp. 2949-2963 ◽  
Author(s):  
Beat Schäffer ◽  
Sabine J. Schlittmeier ◽  
Reto Pieren ◽  
Kurt Heutschi ◽  
Mark Brink ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Laboratory Study ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Time Varying ◽  
Short Term

Equivalent sound level as a predictor for road traffic noise annoyance assessment

2021 ◽  
Vol 263 (2) ◽  
pp. 4622-4633
Author(s):  
Jan Felcyn ◽  
Anna Preis
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Calculated Data ◽  
Sound Level ◽  
Time Structure ◽  
Single Type ◽  
Road Traffic Noise ◽  
The Road ◽  
Noise Annoyance ◽  
Sound Levels

Noise annoyance can be rated either in situ or in laboratory conditions. Regarding the , many papers indicate that only 30% of the variance in people's answers can be explained by sound level values. This value increases when a single type of noise is presented to participants in lab. However, the relationship between time structure of the noise stimulus and annoyance rating is still ambiguous. In this study road traffic noise stimuli with different time structure at three different sound levels were created. Moreover, the psychoacoustical characteristics of them were also computed. The calculated data was compared with results of the listening test in which participants rated each stimulus on the numerical ICBEN scale. Analysis showed that loudness and sound level are the dominant factors, they correlate quite well (~70%) with people's ratings. However, the different time structure of the road traffic noise at the same sound level did not evoke significantly different noise annoyance ratings. Since there are no standards available for loudness measurement, the sound level for the same type of noise remains the simplest factor to reliably predict its impact on people regarding noise annoyance.

scholarly journals Validation of Seasonal ARIMA Models on Road Traffic Noise Measurements

2018 ◽  
Vol 210 ◽  
pp. 05001
Author(s):  
Claudio Guarnaccia ◽  
Joseph Quartieri ◽  
Carmine Tepedino
Keyword(s):  
Road Traffic ◽  
Moving Average ◽  
Traffic Noise ◽  
Periodic Pattern ◽  
Road Traffic Noise ◽  
Related Field ◽  
Autoregressive Integrated Moving Average ◽  
Sound Levels ◽  
Noise Measurements ◽  
Seasonal Arima Models

The Time Series Analysis (TSA) technique is largely used in economics and related field, to understand the slope of a given univariate dataset and to predict its future behaviour. The Seasonal AutoRegressive Integrated Moving Average (SARIMA) models are a class of TSA models that, based on the periodicity observed in the series, build a predictive function that can extend the forecast to a given number of future periods. In this paper, these techniques are applied to a dataset of equivalent sound levels, measured in an urban environment. The periodic pattern will evidence a strong influence of human activities (in particular road traffic) on the noise observed. All the three models will exploit the seasonality of the series and will be calibrated on a partial dataset of 800 data. Once the parameters of the models will be evaluated, all the forecasting functions will be tested and validated on a dataset not used before. The performances of all the models will be evaluated in terms of errors values and distributions, such as introducing some error indexes that explain the peculiar features of the models results.

Road Traffic Noise Prediction Model

2012 ◽  
Vol 3 (4) ◽  
pp. 110-112
Author(s):  
Rahul Singh ◽  
◽  
Parveen Bawa ◽  
Ranjan Kumar Thakur
Keyword(s):  
Prediction Model ◽  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction

SPEED DISTRIBUTION INFLUENCE IN ROAD TRAFFIC NOISE PREDICTION

2013 ◽  
Vol 12 (3) ◽  
pp. 493-501 ◽  
Author(s):  
Gerardo Iannone ◽  
Claudio Guarnaccia ◽  
Joseph Quartieri
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Speed Distribution

Road Traffic Noise Levels at Different Types of Residential Areas in Nibong Tebal, Penang

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Herni Halim ◽  
◽  
Nur Fatin Najiyah Hamid ◽  
Mohamad Firdaus Mahamad Yusob ◽  
Nur Atiqah Mohamad Nor ◽  
...  
Keyword(s):  
Road Traffic ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Residential Areas ◽  
Noise Levels ◽  
Different Types
Sign in / Sign up

Export Citation Format

Share Document