Analysis of the Sound Field in a Room Using Dictionary Learning
Manuel Hahmann, Samuel A. Verburg, Efrén Fernández-Grande · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · 2019
The sound field in a room is often modeled as a superposition of elementary waves, such as plane or spherical waves.These wave expansions provide a powerful means to interpolate or extrapolate the sound field within (and outside) the measurement domain.However, projecting the sound field of a large domain in a room on a planar or spherical wave base yields a high number of very elemental components.We examine the use of dictionary learning to find a set of alternative basis functions that are suitable to represent the sound field enclosed in a room.The resulting dictionary is able to capture the dominant features of the sound field, and represent it using only a sparse set of functions, the dictionary atoms.In this study, high resolution measurements of the sound pressure in a room are simulated and used as a training set to learn a dictionary.We analyze the spatial properties of the learned dictionary, and compare it to simple elementary basis functions such as plane and spherical waves.