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.
Figures & tables
Figure 1: General framework of the AEC system.
Algorithm
Complexity
NLMS
O(N)
RLS
O(N2)
RLS-DCD ( Nu=8 )
O(N2)
FRLS
O(N)
RBD-RLS ( L=32 )
O(32N)
RBD-RLS ( L=64 )
O(64N)
Table 1: COMPREHENSIVE COMPLEXITY OF ADAPTIVE ALGORITHM
Figure 2: Random echo path.
Figure 3: MIS with different value of δ and λ .
Figure 4: Random echo path.
Figure 5: MIS with white noise input.
Figure 6: MIS with colored noise input.
Figure 7: Echo path A.
Figure 8: Echo path B.
Figure 9: MIS with RIR change.
Figure 10: Far-end signal 4 4 4 The speech signal ID is “ _5z9G2AP806bhcI0QF18Qg_farend_singletalk ” .
College of Robotics, Beijing Union University, Beijing 100101, China · Key Laboratory of Intelligent Control and Decision of Complex Systems, and also with the School of Automation, Beijing Institute of Technology, Beijing 100081, China
Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan · National Institute of Technology, Kagawa College, Takamatsu, Kagawa 761-8058, Japan · Yamaha Corporation, Hamamatsu-shi, Shizuoka 430-8650, Japan