A neural-computation method of predicting the early interaural cross-correlation coefficient (IACCE3) for auditoria

A method of predicting the early interaural cross-correlation coefficient (IACCE3) in unoccupied concert halls has been investigated using neural network analysis. Constructional and acoustical data for 36 unoccupied concert halls, in various countries, were utilized for the neural network analyses. A neural network for calculating IACCE3 has been embedded in a standard spreadsheet application so that designers and researchers, without access to specialized neural network software can use the results of the present work. Investigations using the neural network model have shown that IACCE3 predictions are within the subjective difference limen, which is 0.075±0.008. Five concert halls were used to assess the neural network analysis method and the errors between measured and predicted (1−IACCE3) ranged from −0.05 to 0.02. These results indicate that there is a good basis for using trained neural networks to predict IACCE3.