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

Paul Didier, Toon van Waterschoot, Marc Moonen

TL;DR

This work tackles node-specific signal estimation in ad-hoc wireless acoustic sensor networks where topology can change over time. It introduces TI-GEVD-DANSE, a topology-independent, GEVD-based low-rank extension of TI-DANSE that enables distributed MMSE estimation with a rank-$R$ constraint, improving robustness in challenging signal environments. A key contribution is an adaptive filter coefficient normalization to mitigate non-strict convergence and prevent numerical instability during online operation, validated through batch and online experiments in dynamic acoustic scenarios. The results show that the proposed approach achieves centralized-like performance with reduced communication burden and remains stable under topology changes and time-varying statistics, offering practical benefits for scalable distributed speech/audio processing in mobile networks.

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.

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

TL;DR

This work tackles node-specific signal estimation in ad-hoc wireless acoustic sensor networks where topology can change over time. It introduces TI-GEVD-DANSE, a topology-independent, GEVD-based low-rank extension of TI-DANSE that enables distributed MMSE estimation with a rank- constraint, improving robustness in challenging signal environments. A key contribution is an adaptive filter coefficient normalization to mitigate non-strict convergence and prevent numerical instability during online operation, validated through batch and online experiments in dynamic acoustic scenarios. The results show that the proposed approach achieves centralized-like performance with reduced communication burden and remains stable under topology changes and time-varying statistics, offering practical benefits for scalable distributed speech/audio processing in mobile networks.

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.
Paper Structure (10 sections, 19 equations, 2 figures)

This paper contains 10 sections, 19 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. tidanse
  4. Improved Robustness
  5. Non-Strict Convergence of
  6. Normalization strategy
  7. Numerical Experiments
  8. Batch-mode simulations
  9. Online-mode simulations
  10. Conclusion

Figures (2)

  • Figure 1: Batch processing results. The $x$-axis represents the iteration index $i$. Top: $\mathrm{MSE}_{d_k}^i$ averaged over nodes, where the horizontal dotted lines represent the corresponding centralized values. Bottom: $\mathrm{MSE}_{W_k}^i$ averaged over nodes.
  • Figure 2: Online processing results. The $x$-axis represents the index $i$. Top: $\mathrm{MSE}_{d_k}^i$ averaged over nodes. Bottom: $\|\bar{\mathbf{G}}_k^i\|_F$ averaged over nodes.