.png){kind=icon}
Return
Sparse Bayesian Least-Mean-Squares Algorithm
.png){kind=icon}
PRE
AI
DOI：10.1109/TCSII.2023.3244829.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Echo seriously damages the speech communication quality of vehicle communication system and seriously interferes with the normal operation of voice navigation, voice dialing, and other functions of the vehicle-mounted human-machine interaction platform. This brief proposes a sparse Bayesian least-mean-squares (SBLMS) algorithm which is designed to exploit the sparseness of echo channel for improving the quality of echo cancellation. The proposed SBLMS algorithm is derived by performing Bayesian inference in an equivalent linear Gaussian observation model with diagonal proportionate matrix. To avoid algorithmic performance degradation at steady-state, a step-size lower bound is derived for the proposed SBLMS algorithm. Also, a reset parameter method is designed to improve the tracking performance of the proposed SBLMS algorithm in a sudden change scenario. Finally, the good performance of the proposed SBLMS algorithm is confirmed by system identification and acoustic echo cancellation.
Keywords:
Inference algorithms
Mathematical models
Bayes methods
Covariance matrices
Gaussian distribution
Echo cancellers
Adaptive systems
Adaptive echo cancellation
least-mean-squares
sparse system identification
vehicle communication system
Journal
I
IF:
4.9
Papers:
8.8K
Citations:
2.5W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveProportionate Maximum Versoria Criterion-Based Adaptive Algorithm for Sparse System Identification
PREAI
ShareSaveShareSave
View more