Problem
Research questions and friction points this paper is trying to address.
Acoustic Echo Cancellation
Recursive Least Square
Computational Complexity
A Regularized Block Diagonal RLS Algorithm for Acoustic Echo Cancellation
🤖 AI Summary
为解决长滤波器回声消除中RLS算法计算复杂度高的问题，提出了一种正则化块对角RLS算法，通过简化更新过程和并行计算降低复杂度。
📝 Abstract
While the recursive least square (RLS) algorithm is widely used in adaptive filtering applications like acoustic echo cancellation (AEC) due to its fast convergence rate, its high computational complexity severely limit its practical deployment for long filters. In this paper, a regularized block-diagonal RLS (RBD-RLS) algorithm is proposed to address these challenges. By approximating the inverse covariance matrix as a block-diagonal structure, RBD-RLS simplifies the update process into independent parallel computations of sub-blocks, effectively reducing the computational complexity. Additionally, Tikhonov regularization is applied to each sub-blocks for enhance numerical stability. A series of experimental results demonstrate that RBD-RLS maintains good convergence while significantly reducing computational complexity. Moreover, it still exhibits relative robustness in real-world scenarios.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Regularized Block Diagonal RLS
Acoustic Echo Cancellation
Tikhonov Regularization
Computational Complexity
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