无需迭代，单次通信即可实现最优语音分离
Distributed Multichannel Wiener Filtering for Topology-Unconstrained Wireless Acoustic Sensor Networks
- 各节点仅交换低维融合信号，直接计算中心化滤波解
- 单次运行即达中心化性能，延迟与树结构深度相关
- 适用于任意拓扑、混响环境，对模型偏差鲁棒
本文提出一种拓扑无关的分布式多通道维纳滤波（TI-dMWF）算法，用于无线声学传感器网络中的节点级信号估计。该算法使每个节点在不进行迭代估计的情况下，仅通过交换低维融合信号，即可计算出其对应的中心化多通道维纳滤波解。当每个声源被所有节点或单一节点观测时，该方法被证明为最优。理论分析与数值仿真表明，TI-dMWF可在单次运行中达到中心化估计性能。其延迟随剪枝树深度变化，计算复杂度也得到分析。在混响房间中，基于估计的二阶统计量，该算法在多种网络拓扑和观测模型偏差下均表现出良好鲁棒性。
原文摘要 · Abstract (English)
This paper introduces the topology-independent distributed multichannel Wiener filter (TI-dMWF), a novel algorithm for distributed node-specific signal estimation in wireless acoustic sensor networks (WASNs) with unconstrained topologies. The TI-dMWF enables each node in the network to compute its centralized multichannel Wiener filter solution by exchanging only low-dimensional fused signals, without requiring iterative estimation, unlike state-of-the-art approaches such as the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm. The TI-dMWF is proven optimal when each source is observed by either all nodes or only one node. Theoretical analysis and numerical simulations confirm that it achieves centralized estimation performance in a single run. Its latency as a function of the pruned-tree depth and its computational complexity are also analyzed. Its robustness is assessed in reverberant-room simulations under estimated second-order statistics, various network topologies, and deviations from the assumed observability model.
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