A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology

TL;DR

This paper introduces a new distributed beamformer for wireless acoustic sensor networks that is robust to estimation errors and arbitrary topologies, matching centralized performance with reduced communication costs.

Contribution

A novel robust distributed linearly constrained beamformer that maintains centralized performance in arbitrary network topologies and improves robustness to estimation errors.

Findings

Achieves performance equivalent to centralized beamformers.

Demonstrates robustness to RATF and TAD estimation errors.

Reduces communication costs compared to existing methods.

Abstract

We propose a new robust distributed linearly constrained beamformer which utilizes a set of linear equality constraints to reduce the cross power spectral density matrix to a block-diagonal form. The proposed beamformer has a convenient objective function for use in arbitrary distributed network topologies while having identical performance to a centralized implementation. Moreover, the new optimization problem is robust to relative acoustic transfer function (RATF) estimation errors and to target activity detection (TAD) errors. Two variants of the proposed beamformer are presented and evaluated in the context of multi-microphone speech enhancement in a wireless acoustic sensor network, and are compared with other state-of-the-art distributed beamformers in terms of communication costs and robustness to RATF estimation errors and TAD errors.