Sampling Rate Offset Estimation and Compensation for Distributed Adaptive Node-Specific Signal Estimation in Wireless Acoustic Sensor Networks

TL;DR

This paper introduces a method to estimate and compensate for sampling rate offsets in wireless acoustic sensor networks, enabling effective distributed adaptive signal processing despite device asynchrony.

Contribution

It proposes a novel SRO estimation and compensation technique integrated with DANSE, allowing asynchronous devices to perform coherent distributed noise reduction.

Findings

Effective SRO estimation and compensation in WASNs

Improved distributed noise reduction performance

Robustness to device asynchrony

Abstract

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.