Machine-learning-based estimation of absorption coefficients from transfer functions modeled by equivalent sources

Abstract

In the field of room acoustics, it is important to find the absorption coefficients of the wall surface, which is a boundary condition for modeling the room acoustic field. However, it is not easy to measure the acoustic impedances of the entire room because it requires many measurement points near the wall surface. Recently, a method to estimate the acoustic impedance and absorption coefficients by using both measurement and simulation methods has been proposed. However, a large number of measurement points are required to obtain sufficient estimation accuracy. In this study, we proposed estimation method of the sound absorption coefficients using machine learning with virtually increasing the number of microphones. First, the transfer functions at the virtual microphones are obtained from small number of transfer functions based on the sound field modeling by sparse equivalent sources. Then, the both transfer functions at the virtual and real microphones are used as the training data for machine learning. To evaluate estimation accuracy of the proposed method, we conducted the two-dimensional simulation experiments based on the boundary element method.