Reverberation time predictions using neural network analysis

Abstract

The study aims to utilize neural network analysis to develop a method to predict the reverberation time for enclosures—in the low and mid frequencies—by using neural networks that have been trained with constructional and acoustical data. The study begins with the hypothesis that, in practice, reverberation time predictions are too difficult to undertake using existing computer models, and too inaccurate when using other methods. Specifically, the study aims at providing an expeditious and accurate method of predicting the reverberation time of enclosures at the initial design stage. To substantiate the hypothesis, and to bring into effect the aims of the study, assessments are made of the predictive powers of the trained neural networks. The results of the investigations have indicated that there is a good basis for using trained neural networks to predict the reverberation time for enclosures. The results have also shown that neural network analysis can identify those variables that have an effect on the predicted reverberation times.