A logistic prediction model for individual allowable noise levels
1Department of Electrical and Electronic Engineering, Faculty of Engineering, Shinshu University, Nagano, Japan. kennfur@shinshu-u.ac.jp
Abstract:
Annoyance to noise is probably a good measure of the potential for complaint and community action. Each individual's degree of annoyance to noise cannot be accurately predicted from conventional laboratory studies and social surveys. Therefore, a logistic prediction model for individual allowable noise levels has been developed. In a previous paper [K. Furihata et al., Acta Acust. Acust. 93, 73-83 (2007)], an annoyance scale using the most frequently used annoyance ratings related to noise for community noise assessment in Japan was constructed. The dose-response relationships were obtained by actually listening to sounds in a field situation (1474 subjects) carried out at 14 metropolitan cities in Japan. The logistic prediction model was developed using the logistic regression parameters estimated from each cumulative percentage curve of seven annoyance scale scores based on all data (n=2940). To verify the hypothesis, individual relationships between annoyance and L(Aeq, (5 min)) for 24 normal-hearing subjects were constructed using a laboratory experiment. The main conclusion drawn from the results is that if at least one dose-response relation is obtained from each subject, the model can predict whether a sound is annoying or not from its L(Aeq,(5 min)) value. Moreover, validation of the model is needed in a field situation.
Related Concept Videos
Difference from Background: Limit of Detection
The LOD indicates the presence or absence...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Applications of Logarithms
Sound Intensity Level
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and hence a...
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Pharmacodynamic Models: Logarithmic Concentration–Effect Model
