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.

翻译：异构无线声传感器网络中设备间的采样率偏移会阻碍依赖相干信号处理的分布式自适应算法按预期执行。本文提出一种采样率偏移估计与补偿方法，使得分布式自适应节点特定信号估计算法能够部署在由异步设备组成的无线声传感器网络中。首先利用各节点可用信号，基于相干漂移方法盲估计采样率偏移，随后通过频域相移进行补偿。针对加权重叠相加法在分布式自适应节点特定信号估计算法中的实现进行改进，以处理采样率偏移引起的全样本漂移，通过将加权重叠相加过程近似为时域卷积实现逐样本信号传输。在异步无线声传感器网络中目标语音信号估计的分布式降噪场景下，对所提算法的性能进行了评估。