Abstract
Sound field estimation is the process of analyzing and characterizing the distribution of sound waves in a particular physical space. The applications of sound field estimation extend to various areas, including the visualization of acoustic fields, interpolation of room impulse responses, identification of sound sources, capturing sound fields for spatial audio, and spatial active noise control, among other potential uses. In a previous study, a directionally weighted kernel has been used to estimate the sound field, where the priori information of source directions is employed to improve the estimation accuracy. In another separate study, spherical harmonics have been used to represent the sound field. However, the order of spherical harmonic coefficients was limited due to the limited number of microphones. This research introduces a novel method for sound field estimation using multiple microphones to sample a source-free volume. A physics-informed neural network is used to predict the spherical harmonics coefficients and locations of unknown sources to estimate the sound field.
Published Version
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