无需训练数据，仅用少量测量点重建声场。
A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field Reconstruction
- 基于物理方程构建字典，直接约束声场的物理解释性。
- 仅需少量观测点即达主流方法性能，且无需训练数据。
- 适合传感器稀疏、无可用训练数据的声学场景。
声场重建旨在估算未直接测量区域的压力分布。现有方法通常依赖强假设，或受限于数据可得性及物理特性显式建模。本文提出一种零样本、物理信息引导的字典学习方法，仅需少量稀疏测量即可学习字典，无需额外训练数据。通过在优化过程中强制满足赫姆霍兹方程，所提方法确保重建声场为少数具有物理意义原子的线性组合。真实数据评估表明，该方法性能与当前最优字典学习技术相当，优势在于仅需极少声场观测且无需数据集训练。
原文摘要 · Abstract (English)
Sound field reconstruction aims to estimate pressure fields in areas lacking direct measurements. Existing techniques often rely on strong assumptions or face challenges related to data availability or the explicit modeling of physical properties. To bridge these gaps, this study introduces a zero-shot, physics-informed dictionary learning approach to perform sound field reconstruction. Our method relies only on a few sparse measurements to learn a dictionary, without the need for additional training data. Moreover, by enforcing the Helmholtz equation during the optimization process, the proposed approach ensures that the reconstructed sound field is represented as a linear combination of a few physically meaningful atoms. Evaluations on real-world data show that our approach achieves comparable performance to state-of-the-art dictionary learning techniques, with the advantage of requiring only a few observations of the sound field and no training on a dataset.
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