The use of neural network analysis to predict the acoustic performance of large rooms Part I. Predictions of the parameter G utilizing numerical simulations

A method of predicting the G values (the strength factor in dB) in large enclosures, using artificial neural networks, has been investigated. ODEON 3.1 was used to determine the acoustical attributes (including G values) for 110 unoccupied ‘shoebox’ enclosures. One source position and a combination of receiver positions were chosen, and the acoustical quantities were calculated for the 125, 250, 500, 1000 and 2000 Hz octave bands. Neural networks were then trained, verified, and tested using this data together with the size and geometrical proportions of the room. Assessments have been made of the method by comparing the predicted G obtained using neural networks with those calculated using the hybrid ray tracing computer model ODEON 3.1 for enclosures not used in the original training process. The results were also compared with Barron’s revised theory. Predictions of G in this way were in good agreement with verification and test data and Barron’s revised theory predictions. Neural networks trained with data obtained from only a small number of enclosures but with a large number of receiver positions also produced good results. The accuracy of the results for the position-dependent G, at each frequency band, is within the subjective difference limen of the acoustical parameter G, which is ±1 dB. Finally, the results show that neural network predictions based on ray-tracing programs have the potential for finding patterns in the data which could be used to establish rules of thumb to be used in the early stages of a design.