Statistical evaluation of road traffic noise in complex sound environment by using fuzzy inference

In order to evaluate road traffic noise in a sound environment, it is necessary to predict the sound levels at evaluation points based on the observations at a reference point. In actual sound environment, road traffic noise shows various types of probability distribution of non-Gaussian distribution and nonstationary property. Furthermore, there potentially exist various nonlinear correlations in addition to the linear correlation between the sound levels at evaluation and reference points. Consequently, the relationship between both points cannot be represented by a simple linear model based on only the linear correlation and lower order statistics. In this study, a complex sound environment difficult to analyze by using usual structural method is considered. By introducing a nonlinear model based on conditional probability distribution with various correlations between the sound levels, and applying fuzzy inference, an evaluation method for road traffic noise is theoretically proposed in a suitable form for the complex sound environment with nonlinear, non-Gaussian and nonstationary properties. The effectiveness of the proposed method is experimentally confirmed by applying it to the observed road traffic noise data in a complex sound environment.