基于半监督源估计和马尔可夫随机场的声传感器网络失准识别
Misalignment Recognition in Acoustic Sensor Networks using a Semi-supervised Source Estimation Method and Markov Random Fields
摘要：
在本文中，我们认为声源定位的问题，通过声学传感器网络（ASNs）使用一个有前途的，基于学习的技术，适应声学环境。特别是，我们看的情况下，当一个节点在训练过程中从它的位置被移位。由于用于学习定位模型的时间序列与节点位移之后的时间序列之间的失配导致错误的位置估计，因此必须检测位移并且需要识别位移的节点。我们提出了一种方法，考虑由留一节点（LONO）子网络的位置估计的差异，并使用马尔可夫随机场（MRF）框架来推断每个LONO位置估计对齐，未对齐或不可靠的概率，同时考虑固有的噪声估计。这种概率方法优于朴素检测方法，因为它输出归一化值，该归一化值封装了由每个LONO子网络提供的关于读数是否与整个网络不对准的条件信息。实验结果表明，该方法在各种声学条件下识别受损节点的性能是一致的。
英文摘要：
In this paper, we consider the problem of acoustic source localization by acoustic sensor networks (ASNs) using a promising, learning-based technique that adapts to the acoustic environment. In particular, we look at the scenario when a node in the ASN is displaced from its position during training. As the mismatch between the ASN used for learning the localization model and the one after a node displacement leads to erroneous position estimates, a displacement has to be detected and the displaced nodes need to be identified. We propose a method that considers the disparity in position estimates made by leave-one-node-out (LONO) sub-networks and uses a Markov random field (MRF) framework to infer the probability of each LONO position estimate being aligned, misaligned or unreliable while accounting for the noise inherent to the estimator. This probabilistic approach is advantageous over naive detection methods, as it outputs a normalized value that encapsulates conditional information provided by each LONO sub-network on whether the reading is in misalignment with the overall network. Experimental results confirm that the performance of the proposed method is consistent in identifying compromised nodes in various acoustic conditions.
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。
