The prediction of reverberation time using neural network analysis

Abstract

An alternative method of predicting the reverberation time for enclosures, using artificial neural networks, has been investigated. The study begins with the hypothesis that, at the conceptual design stage, reverberation time predictions are too difficult to derive using existing computer models, and too inaccurate when using other methods and that a more explicit and quicker method of predicting reverberation time is required. Assessments are made of the predictive powers of trained neural networks by comparing the predicted reverberation times obtained using neural networks with those using Sabine's and Eyring's ‘classical equations’ and the ray tracing model ODEON 2.6D. The results indicate that there is a good basis for using trained neural networks to predict the reverberation time for enclosures but that 15 input variables are required to achieve accuracy of ±10%. In addition, the results show that neural network analysis can identify those variables which have the greatest effect on the predicted reverberation times.