In a wireless acoustic sensor network (WASN), devices (i.e., nodes) can collaborate through distributed algorithms to collectively perform audio signal processing tasks. This paper focuses on the distributed estimation of node-specific desired speech signals using network-wide Wiener filtering. The objective is to match the performance of a centralized system that would have access to all microphone signals, while reducing the communication bandwidth usage of the algorithm. Existing solutions, such as the distributed adaptive node-specific signal estimation (DANSE) algorithm, converge towards the multichannel Wiener filter (MWF) which solves a centralized linear minimum mean square error (LMMSE) signal estimation problem. However, they do so iteratively, which can be slow and impractical. Many solutions also assume that all nodes observe the same set of sources of interest, which is often not the case in practice. To overcome these limitations, we propose the distributed multichannel Wiener filter (dMWF) for fully connected WASNs. The dMWF is non-iterative and optimal even when nodes observe different sets of sources. In this algorithm, nodes exchange neighbor-pair-specific, low-dimensional (fused) signals estimating the contribution of sources observed by both nodes in the pair. We formally prove the optimality of dMWF and demonstrate its performance in simulated speech enhancement experiments. The proposed algorithm is shown to outperform DANSE in terms of objective metrics after short operation times, highlighting the benefit of its iterationless design.

翻译：在无线声学传感器网络（WASN）中，各设备（即节点）可通过分布式算法进行协作，共同完成音频信号处理任务。本文聚焦于利用网络范围的维纳滤波对节点特定的期望语音信号进行分布式估计。目标是在降低算法通信带宽占用的同时，达到能够访问所有麦克风信号的集中式系统的性能水平。现有解决方案（如分布式自适应节点特定信号估计算法DANSE）通过迭代收敛至多通道维纳滤波器（MWF），该滤波器求解的是集中式线性最小均方误差（LMMSE）信号估计问题。然而，这种迭代过程可能收敛缓慢且不实用。许多方案还假设所有节点观测到相同的目标声源集合，这在实际场景中往往不成立。为克服这些局限，我们针对全连接WASN提出了分布式多通道维纳滤波器（dMWF）。该滤波器具有非迭代特性，且即使在节点观测不同声源集合时仍能保持最优性。在此算法中，节点间交换针对邻居节点对定制的低维（融合）信号，这些信号用于估计节点对双方共同观测声源的贡献分量。我们通过严格理论证明dMWF的最优性，并在模拟语音增强实验中验证其性能。实验表明，所提算法在短时运行后即可在客观指标上超越DANSE，突显了其无迭代设计的优势。