Abstract
We propose a transfer learning framework for sound source reconstruction in Near-field Acoustic Holography (NAH), which adapts a well-trained data-driven model from one type of sound source to another using a physics-informed procedure. The framework comprises two stages: (1) supervised pre-training of a complex-valued convolutional neural network (CV-CNN) on a large dataset, and (2) purely physics-informed fine-tuning on a single data sample based on the Kirchhoff-Helmholtz integral. This method follows the principles of transfer learning by enabling generalization across different datasets through physics-informed adaptation. The effectiveness of the approach is validated by transferring a pre-trained model from a rectangular plate dataset to a violin top plate dataset, where it shows improved reconstruction accuracy compared to the pre-trained model and delivers performance comparable to that of Compressive-Equivalent Source Method (C-ESM). Furthermore, for successful modes, the fine-tuned model outperforms both the pre-trained model and C-ESM in accuracy.
Abstract (translated)
我们提出了一种用于近场声全息（NAH）中声音源重建的迁移学习框架，该框架通过物理信息引导的过程将一个类型的声音源上训练好的数据驱动模型适应到另一个类型的声音源。此框架包含两个阶段：(1) 在大型数据集上对复数卷积神经网络（CV-CNN）进行监督预训练；(2) 基于Kirchhoff-Helmholtz积分，在单个数据样本上仅使用物理信息进行微调。这种方法遵循迁移学习的原则，通过基于物理知识的适应性实现了跨不同数据集的泛化能力。该方法的有效性通过将一个预先在矩形板数据集上训练好的模型迁移到小提琴面板数据集中得到了验证，在这种情况下，与预训练模型相比，重建精度提高了，并且其性能可媲美压缩等效源法（C-ESM）。此外，在成功模式下，微调后的模型不仅比预训练的模型更准确，也超过了C-ESM的准确性。
URL
https://arxiv.org/abs/2507.11070
