2021
DOI: 10.1109/lsp.2021.3093862
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Exponential Hyperbolic Cosine Robust Adaptive Filters for Audio Signal Processing

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Cited by 67 publications
(5 citation statements)
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“…However, it is also crucial to improve robustness against non-Gaussian noises in practical scenarios. In this regard, incorporating ideas proposed in [19,20], particularly regarding iterative implementation, can be considered. Second, if an iterative structure is employed for implementing the proposed algorithm, investigating its tracking performance would be intriguing.…”
Section: Discussionmentioning
confidence: 99%
“…However, it is also crucial to improve robustness against non-Gaussian noises in practical scenarios. In this regard, incorporating ideas proposed in [19,20], particularly regarding iterative implementation, can be considered. Second, if an iterative structure is employed for implementing the proposed algorithm, investigating its tracking performance would be intriguing.…”
Section: Discussionmentioning
confidence: 99%
“…In this paper, setting β to 0.8 achieves the best convergence performance under pulse noise. Krishna Kumar introduced a new robust norm based on the exponential hyperbolic cosine function (EHCF) and developed corresponding adaptive filters based on EHCF [51]. Radhika proposed two adaptive filters based on hyperbolic sine functions that are suitable for impulse noise environments [52].…”
Section: Logarithmic Transformation and Trigonometric Transformationmentioning
confidence: 99%
“…Recently, a new AP-type algorithm, named the Hybrid Affine Projection Algorithm (H-APA) [5], has been proposed, which combines the advantages of the Affine Projection Robust Mixed Norm Algorithm (APRMNA) [6] and APSA to achieve better performance under non-Gaussian noise. Of course, there are also many other methods applied to improve the robustness of adaptive filtering algorithms [7][8][9][10][11][12][13]. For satisfying the contradictory requirements between fast convergence speed and small steady-state misalignment, studies have used various methods to further improve the convergence speed and accuracy of APSA, such as various types of variable step, combined step methods [14][15][16][17][18][19][20][21], etc.…”
Section: Introductionmentioning
confidence: 99%