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arXiv 2408.03752eess.ASeess.SP
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声学传感器网络中具有非重叠潜在子空间的一次性分布式节点特定信号估计

One-Shot Distributed Node-Specific Signal Estimation with Non-Overlapping Latent Subspaces in Acoustic Sensor Networks

Paul Didier, Pourya Behmandpoor, Toon van Waterschoot, Marc Moonen

AI总结:

本文提出一次性无迭代的iDANSE算法,在非重叠潜在子空间的无线声学传感器网络中实现分布式节点特定信号估计,单周期性能媲美集中式算法,且交换信号少,利于实时应用。

摘要:

本文提出了一种名为无迭代DANSE(iDANSE)的一次性算法,用于在完全连接的无线声学传感器网络(WASN)中执行分布式自适应节点特定信号估计(DANSE),该网络部署在具有非重叠潜在信号子空间的环境中。iDANSE算法在单个处理周期内即可匹配集中式算法的性能,同时设备间交换其多通道本地麦克风信号的融合版本。iDANSE相较于现有解决方案的关键优势在于其无迭代特性,这有利于实时应用中的部署,以及设备可以交换比环境中潜在源数量更少的融合信号。所提出的方法在数值模拟中得到了验证,包括一个语音增强场景。

英文摘要:

A one-shot algorithm called iterationless DANSE (iDANSE) is introduced to perform distributed adaptive node-specific signal estimation (DANSE) in a fully connected wireless acoustic sensor network (WASN) deployed in an environment with non-overlapping latent signal subspaces. The iDANSE algorithm matches the performance of a centralized algorithm in a single processing cycle while devices exchange fused versions of their multichannel local microphone signals. Key advantages of iDANSE over currently available solutions are its iterationless nature, which favors deployment in real-time applications, and the fact that devices can exchange fewer fused signals than the number of latent sources in the environment. The proposed method is validated in numerical simulations including a speech enhancement scenario.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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