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Distributed MAP Estimators for Noise Reduction in Fully Connected Wireless Acoustic Sensor Networks

机译：用于完全连接的无线声学传感器网络的降噪的分布式地图估计

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Several noise reduction algorithms have been proposed for wireless acoustic sensor networks, which consist of spatially distributed nodes that are connected via a wireless link. To decrease the required bandwidth and computational complexity, in this paper we propose two iterative distributed maximum a posteriori (MAP) estimators. In the first scheme, each node sequentially updates its estimate, whereas in the second scheme, all nodes simultaneously update their estimates. Based on simulations in a reverberant room with three nodes, we have compared the noise reduction performance of the proposed distributed MAP estimators with the centralized MAP estimator, where each node has access to all signals, and the local MAP estimator, where each node only has access to its own signals. The simulation results show that the proposed distributed estimators result in a good noise reduction performance, while decreasing the computational complexity compared to the centralized estimator.
机译：已经提出了用于无线声学传感器网络的几种降噪算法，其包括通过无线链路连接的空间分布式节点。为了减少所需的带宽和计算复杂性，在本文中，我们提出了两个迭代分布式最大后的后验（MAP）估计。在第一种方案中，每个节点顺序地更新其估计，而在第二方案中，所有节点同时更新其估计。基于具有三个节点的混响室中的模拟，我们将所提出的分布式地图估计的降噪性能与集中式地图估计器进行了比较，其中每个节点可以访问所有信号，以及每个节点的本地地图估计器访问自己的信号。仿真结果表明，与集中估计器相比，所提出的分布式估计器导致良好的降噪性能，同时降低计算复杂性。

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