Prediction of outdoor sound transmission loss with an artificial neural network

Abstract

An artificial neural network is developed for rapid prediction of sound transmission loss (TL) during propagation outdoors. The network predicts TL for a nonturbulent atmosphere from inputs involving the source/receiver propagation geometry (height range: 0–5 m, horizontal separation distance: 100–900 m), source frequency (range: 20–200 Hz), ground properties, and atmospheric refractive profile characteristics. A parabolic equation (PE) code generates the training and test data sets for the network. To ensure that a minimal set of input parameters is used in the network training, a nondimensional version of the PE and accompanying boundary, initial, and atmospheric conditions is developed. A total of 10 independent, nondimensional input parameters are found to be necessary for the training. Approximately 27,000 random cases involving these 10 parameters are generated used to train networks with varying numbers of neurons. The root mean square (RMS) error between random test cases solved by the PE and corresponding neural network predictions was 2.42 dB when a sufficient number of neurons (about 44) are included in the hidden layer. Also, only 18% of the cases resulted in RMS errors that were greater than 2 dB.