This paper presents a Gaussian-based distributed speech presence probability (DSPP) estimator which is applied in fully connected wireless acoustic sensor networks (WASNs). In WASNs, we are primarily interested in optimally utilizing all available information of recorded signals. In this work, under the Gaussian statistical assumption of signals, each node computes the DSPP using its own local signals along with the compressed signals from other nodes. We evaluate the effect of DSPP estimation on noise reduction from both the simulated and the real recorded signals. The performance of the proposed DSPP estimator is compared to that of local SPP estimation, where each node only uses its noisy signals, and to that of centralized SPP estimation, where each node uses all recorded noisy signals of the whole network. It is shown that the proposed method exhibits good performance, while the computational complexity is considerably reduced.

中文翻译：

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全连接无线声学传感器网络中的分布式语音存在概率估计器

本文提出了一种基于高斯的分布式语音存在概率 (DSPP) 估计器，该估计器应用于全连接无线声学传感器网络 (WASN)。在 WASN 中，我们主要对优化利用记录信号的所有可用信息感兴趣。在这项工作中，在信号的高斯统计假设下，每个节点使用自己的本地信号以及来自其他节点的压缩信号来计算 DSPP。我们从模拟和真实记录的信号中评估了 DSPP 估计对降噪的影响。所提出的 DSPP 估计器的性能与本地 SPP 估计的性能进行了比较，其中每个节点仅使用其噪声信号，以及集中式 SPP 估计的性能，其中每个节点使用整个网络的所有记录的噪声信号。