掩蔽小波散射变换神经场用于声场重建
Masked Wavelet Scattering Transform Neural Field for Sound Field Reconstruction
AI总结：
提出一种利用小波散射变换作为多尺度特征提取器，结合神经场优化和掩蔽策略，实现稀疏观测下声场重建的方法，并在HRTF上采样中验证有效性。
摘要：
在本文中，我们提出了一种重建框架，利用小波散射变换（WST）作为多尺度特征提取器，在稀疏观测条件下施加统计先验。重建问题被表述为一个优化任务，并使用神经场求解，将WST纳入训练损失函数。作为概念验证，我们在HRTF上采样上验证了所提出的方法。对WST系数应用掩蔽策略，形成两阶段过程。第一阶段从小的多受试者数据集中学习二元掩码，第二阶段将学习到的掩码应用于单个HRTF的WST系数，以在重建过程中保留信息性统计结构。与基线方法的验证（同时也作为框架不同组件的消融研究）证明了所提出方法的有效性。
英文摘要：
In this paper, we propose a reconstruction framework that leverages the Wavelet Scattering Transform (WST) as a multi-scale feature extractor to impose statistical priors under sparse observation conditions. The reconstruction problem is formulated as an optimization task and solved using a neural field, with the WST incorporated into the training loss function. As a proof of concept, we validate the proposed method on HRTF upsampling. A masking strategy is applied to the WST coefficients, resulting in a two-phase procedure. The first phase learns a binary mask from a small multi-subject dataset, while the second phase applies the learned mask to the WST coefficients of an individual HRTF to preserve informative statistical structures during reconstruction. Validation against baseline methods, which also serve as an ablation study of the different components of the framework, demonstrates the effectiveness of the proposed approach.
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