Geometry-Informed Distributed Acoustic Scene Understanding

2026-09-07

AI Summary

针对多房间环境中的声学场景理解难题，提出了一种基于几何信息的分布式框架，利用分布式的麦克风、音频频谱变换器及拓扑感知图神经网络融合时空声学特征。

Abstract

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.

Problem

• Acoustic Scene Understanding
• Multi-room Environments
• Distributed Microphones

Innovation

• geometry-informed
• distributed microphones
• audio spectrogram transformer
• topology-aware graph neural network
• frozen large language model