A neural network analysis of the effect of geometric variables on concert hall G values

This paper deals with the schematic design of large rooms (concert halls) using artificial neural networks. Previously published research has shown that neural networks can be used to predict the acoustical attributes (G, C80, LF and RT60) of concert halls. Neural network analyses have been undertaken, and the changes in neural network predictions of the parameter strength factor, G, according to the variations of concert hall design variables, has been investigated. The results of the analyses are difficult to describe simply as there appears to be a non-linear relationship between some of the many input variables. Seven concert halls have been used to show how the change in certain geometric variables will influence G values.