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M-Estimate Based Normalized Subband Adaptive Filter Algorithm: Performance Analysis and Improvements
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DOI：10.1109/TASLP.2019.2950597.png)
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摘要
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This article studies the mean and mean-square behaviors of the M-estimate based normalized subband adaptive filter algorithm (M-NSAF) with robustness against impulsive noise. Based on the contaminated-Gaussian noise model, the stability condition, transient and steady-state results of the algorithm are formulated analytically. These analysis results help us to better understand the M-NSAF performance in impulsive noise. To further obtain fast convergence and low steady-state estimation error, we derive a variable step size (VSS) M-NSAF algorithm. This VSS scheme is also generalized to the proportionate M-NSAF variant for sparse systems. Computer simulations on the system identification in impulsive noise and the acoustic echo cancellation with double-talk are performed to demonstrate our theoretical analysis and the effectiveness of the proposed algorithms.
Keyword:
Convergence
Echo cancellers
Steady-state
Estimation error
Speech processing
Adaptive systems
Acoustic echo cancellation
impulsive noise
M-estimate
subband adaptive filter
variable step size (VSS)
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2.6K
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