Estimation of classroom reverberation time using convolutional neural network

The classroom, as one of the most important educational environments, plays a major role in students' learning. Reverberation time is one of the most important acoustic parameters affecting the sound quality inside the environment. The inefficiency of classical formulas such as Sabin, led to the study of the use of machine learning methods as an alternative method for predicting the reverberation time of the environment. In this research, first, using the methods based on geometric acoustics and using Odeon software, the required data sets are collected at 500 and 2000 Hz frequencies. In this dataset, 4 classrooms with a rectangular space were used, along with elements such as desks, chairs, windows and doors. The convolutional neural network used to provide a machine learning based system. In this study, using a convolutional neural network for a frequency of 500 Hz with a coefficient of determination of 93% and for a frequency of 2000 Hz, a coefficient of determination of 95% was obtained.