论文标题
无线声音传感器网络中分布式自适应节点特异性信号估计的采样率抵消估计和补偿
Sampling Rate Offset Estimation and Compensation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Acoustic Sensor Networks
论文作者
论文摘要
在异质无线声音传感器网络（WASN）中，在设备之间的采样率偏移（SRO）可以阻止分布式自适应算法在依赖相干信号处理时按照预期执行的能力。在本文中，我们提出了一种SRO估计和补偿方法，以允许在WASN中部署分布式自适应节点特异性信号估计（DANSE）算法，该算法由异步设备组成。每个节点上可用的信号首先是在基于相干拖车的方法中用于盲目估计SRO的，然后通过频域中的相移来补偿。引入了对DANSE的加权重叠ADD（WOLA）实现的修改，以解释SRO诱导的全样本漂移，从而通过近似Wola过程作为时间域卷积，可以通过近似样本信号传输。在降低分布式降噪的背景下，评估了所提出的算法的性能，以估计异步中的目标语音信号。
Sampling rate offsets (SROs) between devices in a heterogeneous wireless acoustic sensor network (WASN) can hinder the ability of distributed adaptive algorithms to perform as intended when they rely on coherent signal processing. In this paper, we present an SRO estimation and compensation method to allow the deployment of the distributed adaptive node-specific signal estimation (DANSE) algorithm in WASNs composed of asynchronous devices. The signals available at each node are first utilised in a coherence-drift-based method to blindly estimate SROs which are then compensated for via phase shifts in the frequency domain. A modification of the weighted overlap-add (WOLA) implementation of DANSE is introduced to account for SRO-induced full-sample drifts, permitting per-sample signal transmission via an approximation of the WOLA process as a time-domain convolution. The performance of the proposed algorithm is evaluated in the context of distributed noise reduction for the estimation of a target speech signal in an asynchronous WASN.
