用稀疏麦克风数据重建3D声场，提升空间音频建模精度。
SF-Flow: Sound field magnitude estimation via flow matching guided by sparse measurements
- 基于流匹配的生成框架，结合3D U-Net与集合编码器
- 在1kHz内实现高精度声场重构，训练速度更快
- 适用于任意数量稀疏输入，适合声学空间建模场景
从稀疏麦克风测量中重建3D声场是一个基本但病态的问题，本文通过声学传输函数（ATF）幅度估计来解决。ATF幅度包含物理空间的关键听觉与声学特性，可用于房间表征与校正。尽管近期生成范式如流匹配（FM）在语音和音乐生成中表现优异，其在空间音频中的潜力仍待挖掘。我们提出一种新型3D ATF幅度重建框架，作为引导生成任务，采用由排列不变集合编码器条件化的3D U-Net。该架构可处理任意数量的稀疏输入，同时利用FM稳定高效的训练特性。实验表明，SF-Flow在高达1kHz的频率范围内实现高精度重建，训练速度显著快于自编码器基线，并随数据集规模增大持续提升性能。
原文摘要 · Abstract (English)
Reconstructing a 3D sound field from sparse microphone measurements is a fundamental yet ill-posed problem, which we address through Acoustic Transfer Function (ATF) magnitude estimation. ATF magnitude encapsulates key perceptual and acoustic properties of a physical space with applications in room characterization and correction. Although recent generative paradigms such as Flow Matching (FM) have achieved state-of-the-art performance in speech and music generation, their potential in spatial audio remains underexplored. We propose a novel framework for 3D ATF magnitude reconstruction as a guided generation task, with a 3D U-Net conditioned by a permutation-invariant set encoder. This architecture enables reconstruction from an arbitrary number of sparse inputs while leveraging the stable and efficient training properties of FM. Experimental results demonstrate that SF-Flow achieves accurate reconstruction up to \SI{1}{kHz}, trains substantially faster than the autoencoder baseline, and improves significantly with dataset size.
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