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Physics-Informed Machine Learning For Sound Field Estimation

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arxiv 2408.14731 v1 pith:ENW4QHUY submitted 2024-08-27 cs.SD eess.AS

Shoichi Koyama , Juliano G. C. Ribeiro , Tomohiko Nakamura , Natsuki Ueno , Mirco Pezzoli This is my paper · ORCID
classification cs.SDeess.AS
keywords estimationsoundfieldlearningmachineinterpolationphysicalphysics-informed
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The area of study concerning the estimation of spatial sound, i.e., the distribution of a physical quantity of sound such as acoustic pressure, is called sound field estimation, which is the basis for various applied technologies related to spatial audio processing. The sound field estimation problem is formulated as a function interpolation problem in machine learning in a simplified scenario. However, high estimation performance cannot be expected by simply applying general interpolation techniques that rely only on data. The physical properties of sound fields are useful a priori information, and it is considered extremely important to incorporate them into the estimation. In this article, we introduce the fundamentals of physics-informed machine learning (PIML) for sound field estimation and overview current PIML-based sound field estimation methods.

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  1. A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field Reconstruction

    eess.AS 2024-12 conditional novelty 5.0 of 10

    A zero-shot dictionary learning algorithm that enforces the Helmholtz equation on its atoms reconstructs sound fields from sparse measurements with accuracy comparable to a supervised dictionary learning baseline.

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