  In time-cost scale model studies, predicting acoustic performance by using
simulation methods is a commonly used method that is preferred. In this field,
building acoustic simulation tools are complicated by several challenges,
including the high cost of acoustic tools, the need for acoustic expertise, and
the time-consuming process of acoustic simulation. The goal of this project is
to introduce a simple model with a short calculation time to estimate the room
acoustic condition in the early design stages of the building. This paper
presents a working prototype for a new method of machine learning (ML) to
approximate a series of typical room acoustic parameters using only geometric
data as input characteristics. A novel dataset consisting of acoustical
simulations of a single room with 2916 different configurations are used to
train and test the proposed model. In the stimulation process, features that
include room dimensions, window size, material absorption coefficient,
furniture, and shading type have been analysed by using Pachyderm acoustic
software. The mentioned dataset is used as the input of seven machine-learning
models based on fully connected Deep Neural Networks (DNN). The average error
of ML models is between 1% to 3%, and the average error of the new predicted
samples after the validation process is between 2% to 12%.
