Topology-Independent GEVD-Based Distributed Adaptive Node-Specific Signal Estimation in Ad-Hoc Wireless Acoustic Sensor Networks

TL;DR

This paper introduces a topology-independent GEVD-based distributed adaptive algorithm for node-specific signal estimation in ad-hoc wireless acoustic sensor networks, addressing stability issues with an adaptive normalization strategy.

Contribution

It presents a novel GEVD-based extension of the TI-DANSE algorithm with a normalization method to ensure stable convergence in dynamic scenarios.

Findings

Normalization improves numerical stability

Algorithm performs well in dynamic acoustic environments

Stable convergence achieved with the proposed method

Abstract

A low-rank approximation-based version of the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm is introduced, using a generalized eigenvalue decomposition (GEVD) for application in ad-hoc wireless acoustic sensor networks. This TI-GEVD-DANSE algorithm as well as the original TI-DANSE algorithm exhibit a non-strict convergence, which can lead to numerical instability over time, particularly in scenarios where the estimation of accurate spatial covariance matrices is challenging. An adaptive filter coefficient normalization strategy is proposed to mitigate this issue and enable the stable performance of TI-(GEVD-)DANSE. The method is validated in numerical simulations including dynamic acoustic scenarios, demonstrating the importance of the additional normalization.