Improving the efficiency of mean sound level calculations through importance sampling.

Abstract

Outdoor sound levels vary in space and time due to randomness in acoustic source emissions and environmental effects on wave propagation. Sound levels may also appropriately be viewed as random due to uncertainties in the modeling process. To assess the impact of the randomness on sound level predictions, Monte Carlo sampling of the model input space can be used; that is, equally likely sets of model inputs are generated, to which a sound propagation model is applied repeatedly. To improve the efficiency of the calculations, it is highly desirable to sample primarily the acoustical frequency bands and environmental conditions (such as downwind propagation and hard ground surfaces) contributing most strongly to the sound level. Importance sampling techniques, which adaptively focus sampling effort on the most important parts of the input parameter space, can be used for this purpose. This paper explores application of importance sampling to prediction of sound level statistics. The technique is found to be...