Step-size control for acoustic echo cancellation filter—an overview
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
Acoustic Echo and Noise Control: A Practical Approach
Acoustic Echo and Noise Control: A Practical Approach
A new class of gradient adaptive step-size LMS algorithms
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
A novel kurtosis driven variable step-size adaptive algorithm
IEEE Transactions on Signal Processing
On the design of LMS-based channel estimators using the Doppler spread parameter
Digital Signal Processing
Efficient Artifact Elimination in Cardiac Signals using Variable Step Size Adaptive Noise Cancellers
International Journal of Measurement Technologies and Instrumentation Engineering
Light-weight Online Predictive Data Aggregation for Wireless Sensor Networks
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
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Two new gradient-based variable step size least-mean-square (VSSLMS) algorithms are proposed on the basis of a concise assessment of the weaknesses of previous VSSLMS algorithms in high-measurement noise environments. The first algorithm is designed for applications where the measurement noise signal is statistically stationary and the second for statistically nonstationary noise. Steady-state performance analyses are provided for both algorithms and verified by simulations. The proposed algorithms are also confirmed by simulations to obtain both a fast convergence rate and a small steady-state excess mean square error (EMSE), and to outperform existing VSSLMS algorithms. To facilitate practical application, parameter choice guidelines are provided for the new algorithms.