Mean-Square Performance Analysis of Noise-Robust Normalized Subband Adaptive Filter Algorithm

2017/11/29 by Yi Yu, Yu, Yi, Haiquan Zhao +7
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #FOS: Electrical engineering #Image and Signal Denoising Methods #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1711.11413

openalex publication_date 2017/11/29 · openalex created_date 2017/12/22 · openalex updated_date 2026/07/28

Abstract

This paper studies the statistical models of the noise-robust normalized subband adaptive filter (NR-NSAF) algorithm in the mean and mean square deviation senses involving transient-state and steady-state behavior by resorting to the method of the vectorization operation and the Kronecker product. The analysis method does not require the Gaussian input signal. Moreover, the proposed analysis removes the paraunitary assumption imposed on the analysis filter banks as in the existing analyses of subband adaptive algorithms. Simulation results in various conditions demonstrate the effectiveness of our theoretical analysis. For a special form of the algorithm, the proposed steady-state expression is also better accurate than the previous analysis.

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