Spherical Maps of Acoustic Properties as Feature Vectors in Machine-Learning-Based Estimation of Acoustic Parameters

Abstract

This work suggests a method of presenting information about the acoustical and geometric properties of a room as spherical images to a machine-learning algorithm to estimate acoustical parameters of the room. The approach has the advantage that the spatial distribution of the properties can be presented in a generic and potentially compact way to machine learning methods. The estimation of reverberation time T 60 is used as a proof-of-concept study here. The distribution of absorptive material is presented as a spherical map of feature values, in which each value is formed by calculating the equivalent absorption area visible through the corresponding facet of a polyhedron as seen from the polyhedron's center point. The pixel values are then used as feature vectors, and the real measured T 60 values of corresponding rooms are used as target data. This work presents the method and trains a set of neural networks with di erent spherical map resolutions using a dataset composed of real-world acoustical measurements of a single room with 831 di erent configurations of furniture and absorptive materials. The estimation of reverberation time using the proposed approach exhibits a much higher accuracy compared to simple analytic methods, which proves the validity of the approach.