Neural network predictions of speech levels in university classrooms

At the schematic design stage of a classroom there is a need for an expeditious and accurate method of predicting the distribution of sound levels (speech levels). The objective of this work is to investigate the possibility of developing a method of predicting the sound propagation (SP) in university classrooms, using artificial neural networks. Constructional and acoustical data for 34 randomly chosen unoccupied University of British Columbia (UBC) classrooms were used for the neural network analyses. One source position, both directional and omnidirectional sources, and a number of listener positions were chosen in each classroom making a combination of 182 cases available to train the neural networks. Assessments have been made of the method by comparing the predicted sound propagation obtained using neural networks with measured values, with predictions made using Barronʹs revised theory and the Hopkins–Stryker equation. The results indicate that there is a good basis for using trained neural networks to predict the distribution of sound levels in empty classrooms. The results also indicate that neural networks trained with variables which have a causal relationship to the acoustical quality of the UBC classrooms produce reliable and accurate predictions. RMS errors for Sound Propagation, in each of the frequency bands, are within the subjective difference limen for steady-state sound pressure levels, which is about 1dB (i.e. ΔEE=0.26 where E is energy density).