Robust Recursive Least M-Estimate Adaptive Filter for the Identification of Low-Rank Acoustic Systems

Hongsen He; Jingdong Chen; Jacob Benesty; Yi Yu

ICASSP 2021accepted0 citations

Robust Recursive Least M-Estimate Adaptive Filter for the Identification of Low-Rank Acoustic Systems

Hongsen He, Jingdong Chen, Jacob Benesty, Yi Yu

Abstract

To identify acoustic systems (which are low-rank in nature) in non-Gaussian and Gaussian noise, a robust recursive least M-estimate adaptive filtering algorithm is developed in this paper by applying the nearest Kronecker product to decompose the acoustic impulse response. Two M-estimators, i.e., the Cauchy and Welsch estimators, are employed to define the cost function of the adaptive filter, leading to a class of numerically stable adaptive filtering algorithms, which are robust to non-Gaussian noise. The effectiveness of the developed algorithm is validated in acoustic environments with both Gaussian and non-Gaussian noise.

BibTeX

@inproceedings{icassp2021_robustrecursivel,
  title = {Robust Recursive Least M-Estimate Adaptive Filter for the Identification of Low-Rank Acoustic Systems},
  author = {Hongsen He and Jingdong Chen and Jacob Benesty and Yi Yu},
  booktitle = {ICASSP 2021},
  year = {2021}
}