分布式麦克风+几何信息，让声学场景理解更准更连贯。
Geometry-Informed Distributed Acoustic Scene Understanding
- 用分布麦克风和图神经网络融合声学时空特征
- 在模拟遮挡下空间一致性提升，优于集中式基线
- 适合需要物理一致场景推理的智能系统
多房间环境中的声学场景理解极具挑战。现有系统多依赖单一集中式麦克风阵列，常因墙体和门阻隔信号而失效。为此，我们提出一种几何感知的分布式声学场景理解框架。系统利用分布式麦克风，通过音频频谱图变换器与拓扑感知图神经网络融合时空声学特征，并解码为离散语义三元组。最后，冻结的大语言模型将这些符号化观测与环境几何信息结合，实现空间理解，推断合理缺失的事件转换，并生成物理上自洽的场景叙述。在自建多房间仿真器上的实验表明，该框架优于集中式基线，在模拟遮挡条件下显著提升空间一致性。
原文摘要 · Abstract (English)
Acoustic scene understanding in multi-room environments is a difficult task. Most existing systems use a single centralized microphone array, and they often fail because walls and doors block sound signals. To address this challenge, we propose a geometry-informed distributed acoustic scene understanding framework. Our system leverages distributed microphones and uses an audio spectrogram transformer and a topology-aware graph neural network to fuse spatio-temporal acoustic features. Then, these features are decoded into discrete semantic triplets. Finally, a frozen large language model combines these symbolic observations with the environmental geometry. This allows the system to perform spatial understanding, infer plausible missing transitions, and generate a physically consistent narrative of the scene. Experiments on a custom multi-room simulator demonstrate that our framework outperforms centralized baselines and improves spatial consistency under simulated occlusion.
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