Online Segmented Beamforming via Dynamic Programming

2026-05-08

AI Summary

This work addresses the limitations of conventional beamforming in dynamic acoustic environments, where fixed-time-window covariance matrix estimation fails to track time-varying interference and moving sound sources, leading to degraded suppression performance. The authors propose an online segmented beamforming approach that, for the first time, integrates dynamic programming into adaptive beamforming. By employing data-driven temporal segmentation under causal constraints, the method detects abrupt environmental changes in real time and resets the covariance estimation window accordingly. Coupled with online sample covariance estimation and Capon beamforming, this framework adaptively enforces local stationarity. Experimental results demonstrate that the proposed method significantly outperforms traditional fixed-window approaches in both highly reverberant simulated and real-world scenarios, effectively enhancing tracking and suppression of moving interferers.

Abstract

In dynamic acoustic environments characterized by time-varying interferers and moving sources, effective beamforming requires accurately identifying stationary regions over time. Traditional Capon beamformers rely on the instantaneous ensemble covariance matrix, which is inaccessible in practice. Practical implementations overcome this by estimating the sample covariance matrix (SCM) through averaging over a block of temporal samples. However, in non-stationary settings, a naive batch approach fails. Moving interferers smear the SCM, causing the beamformer to place nulls in outdated locations while failing to track newly active interferers, thereby degrading its nulling capabilities. To address this fundamental limitation, an Online Segmented Beamformer is proposed. This algorithm incorporates data-driven temporal segmentation to causally minimize output power while dynamically adapting the SCM estimation windows to local stationarity. By framing the problem through the lens of dynamic programming, the proposed method tracks abrupt environmental changes and resets covariance estimates in real-time. We validate the performance of this framework in a complex, reverberant simulated acoustic environment and in highly reverberant real world experiments, demonstrating its superiority over fixed-window adaptive methods.

Problem

• beamforming
• non-stationary environments
• sample covariance matrix
• interferer tracking
• temporal segmentation

Innovation

• Online Segmented Beamforming
• Dynamic Programming
• Sample Covariance Matrix
• Non-stationary Acoustic Environments
• Temporal Segmentation