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A filter proportionate LMS algorithm based on the arctangent framework for sparse system identification

SIGNAL IMAGE AND VIDEO PROCESSING 2024年第1期18卷 335-342页

作者： Rosalin Patnaik, Ansuman KIIT Deemed Univ Sch Elect Engn Bhubaneswar India

For a physical sparse system identification issue, this brief proposes a filter proportionate arctangent framework-based least mean square (FP-ALMS) algorithm. The ALMS algorithm has significant robustness against impulsive noise, whereas the filter proportionate concept when utilized in combination with the ALMS takes advantage of the sparse feature to accelerate convergence time. As a result, it turns out that the FP-ALMS algorithm has greater robustness and convergence speed in an impulsive environment. Finally, simulation outcomes demonstrate that the novel FP-ALMS algorithm outperforms other existing algorithms in terms of robustness in an impulsive environment, convergence rate, and steady-state error for sparse system identification.

关键词： Sparse adaptive filter proportionate algorithm Nonlinear system identification Impulsive noise

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General Robust proportionate NSAF algorithm With a Step-Size Converter

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第2期70卷 846-850页

作者： Huang, Zongxin Yu, Yi de Lamare, Rodrigo C. He, Hongsen Li, Ke Southwest Univ Sci & Technol Sch Informat Engn Robot Technol Used Special Environm Key Lab Sichua Mianyang Peoples R China CETUC PUC Rio Rio De Janeiro Brazil

To identify sparse systems in the presence of impulsive noises, we propose a general robust proportionate normalized subband adaptive filtering (R-PNSAF) algorithm. Furthermore, to achieve fast convergence and low steady-state misadjustment, we develop a step-size converter (SSC) for R-PNSAF which results in the SSC-R-PNSAF algorithm, which selects the optimal step-size by comparing the mean square deviation at each iteration of the algorithm under given different step-sizes. Simulation results demonstrate the superiority of the proposed scheme in the $\alpha $ -stable noise scenario over the competing techniques.

关键词： Convergence Steady-state Approximation algorithms Sparse matrices Computational complexity Additive noise Acoustics Impulsive noise proportionate algorithm sub-band adaptive filter step-size converter

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An improved proportionate arctangent framework based LMS/F algorithm for system identification

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APPLIED ACOUSTICS 2023年 214卷

作者： Patnaik, Ansuman Nanda, Sarita KIIT Deemed Univ Sch Elect Engg Bhubaneswar India

This paper proposes an improved proportionate arctangent framework that relies on a least mean square/fourth (IPALMS/F) algorithm for the physical sparse system identification problem. The ALMS/F algorithm has sig-nificant robustness against impulsive noise, whereas the improved proportionate concept when utilized with the ALMS/F takes advantage of the sparse characteristic to increase convergence time. Finally, the IPALMS/F al-gorithm is implemented using a recursive adaptive sparse exponential functional link neural network (RASETFLN) nonlinear filter and is named the RASETFLN-IPALMS/F algorithm which resulted in enhanced performance compared to other existing filters in terms of robustness in an impulsive environment, convergence rate, and steady-state error for system identification and acoustic echo cancellation.

关键词： Functional link neural network (FLN) Sparse adaptive filter proportionate algorithm Nonlinear system identification Echo cancellation Impulsive noise

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Recursive adaptive filtering algorithms for sparse channel identification and acoustic noise reduction

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2023年第1期114卷 51-73页

作者： Bendoumia, Redha Hassani, Islam Guessoum, Abderrezak Univ Blida 1 Detect Informat & Commun Lab DIC Dept Elect Blida Algeria Univ Blida 1 Signal Proc & Image Lab LATSI Dept Elect Blida Algeria

This study addresses impulse response identification and acoustic noise cancellation problems using recursive blind source separation (BSS) techniques based sparse adaptive filtering algorithms. The two-channel adaptive filtering feedback algorithms have been proposed to resolve two problems of noise reduction and speech enhancement when the acoustical mixing system is characterized by dispersive impulse responses. In this paper, three recent NLMS-based sparse adaptive filtering algorithms are implemented on two-channel feedback BSS structures. To evaluate their convergence speed property, we use system mismatch and segmental mean square error criteria. We also use the segmental signal-to-noise ratio and cepstral distance criteria to validate the performance of the presented algorithms in noise reduction and speech enhancement properties. We also have tested these sparse recursive versions with real-life speech signals in various noisy conditions. The obtained results show the good performances of these algorithms compared with the non-sparse versions.

关键词： Sparse impulse response Adaptive algorithm Mean square error proportionate algorithm Cepstral distance

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New sub-band proportionate forward adaptive algorithm for noise reduction in acoustical dispersive-and-sparse environments

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APPLIED ACOUSTICS 2021年 175卷 107822-107822页

作者： Bendoumia, Redha Univ Blida 1 Image & Signal Proc Lab Dept Elect Blida Algeria

In this study, we address the problem of acoustic noise reduction by two-channel forward adaptive filtering algorithm. In noise reduction applications, the normalized least mean square (NLMS) algorithm is recently implemented on the two-channel blind source separation (BSS) technique. Exactly in this paper, we propose a new implementation of two-channel forward BSS technique based on proportionate adaptive filtering algorithm in sub-band form. This new implementation is proposed exactly to resolve the problem of proportionate forward NLMS algorithm in term of convergence rate and speech quality when the acoustic environment is characterized by dispersive or sparse impulse responses. To prove the good performance of our proposed sub-band proportionate Forward NLMS algorithm (SP-FNLMS), we compare its performances with the full-band proportionate and non-proportionate versions exactly in four acoustical environments, more dispersive, dispersive, sparse and finally more sparse. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Convergence rate proportionate algorithm Sub-band decomposition Forward BSS

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A Robust M-Shaped Error Weighted algorithms for Censored Regression

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第1期39卷 324-343页

作者： Zhao, Feng Zhao, Haiquan Wang, Wenyuan Minist Educ Key Lab Magnet Suspens Technol & Maglev Vehicle Chengdu Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

In reality, the range of sensor response is limited in many sensor systems due to the saturation characteristics of the sensor. That is, the value exceeding the sensor response range is not observed. Using traditional adaptive algorithms to identify the system of this type may lead to the performance degradation. To address this problem, the censored regression algorithms have been proposed. However, when the mixed sub-Gaussian and super-Gaussian/impulsive noises occur, these algorithms may fail to work. To overcome these drawbacks, a family of robust M-shaped (FRMS) functions for censored regression (CR-FRMS) is proposed in this paper. When the system to be identified exhibits a certain degree of sparsity, the CR-FRMS algorithm cannot fully utilize the characteristics of the sparse system. Therefore, in this paper, proportionate FRMS (PFRMS) algorithm based on -norm constraint for censored regression -CRPFRMS) is also proposed accordingly. The simulations using Gaussian white noise as the input signal and the non-Gaussian mixed noise as the background noise show that the proposed algorithm performs better than other algorithms.

关键词： A family of robust M-shaped (FRMS) functions l(0)-Norm constraint proportionate algorithm Censored regression Adaptive filtering

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Acoustic noise reduction by new two-channel proportionate forward symmetric adaptive decorrelating algorithms in sparse systems

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APPLIED ACOUSTICS 2018年 137卷 69-81页

作者： Bendoumia, Redha Djendi, Mohamed Univ Blida 1 Signal Proc & Image Lab LATSI Route SoumaaBP 270 Blida 09000 Algeria

In several previous works, the two-channel adaptive filtering algorithms have been proposed and combined with forward-and-backward blind source separation structures for acoustic noise reduction and speech enhancement when the impulse responses are dispersive. In this paper, firstly we present all mathematical formulations of forward symmetric adaptive decorrelation algorithm based on normalized step-sizes control. Secondly, we propose three new proportionate algorithms that improve the convergence rate of cross-adaptive filters when the impulse responses of convolutive mixing system are sparse. To validate the good performance of proposed algorithms in term of noise reduction and speech enhancement properties, we do intensive experiments based on several criteria. To evaluate exactly the convergence speed property, we use the system mismatch and segmental mean square error criteria. We use also the cepstral distance and segmental signal-to-noise ratio to evaluate the quality of enhanced speech output. The obtained results have shown good performances of the proposed proportionate algorithms in comparison with the two-channel normalized decorrelation and forward NLMS algorithms with sparse systems.

关键词： Acoustic noise Sparse system proportionate algorithm Decorrelation SegMSE criterion Convergence rate property

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A Signal Decorrelation PNLMS algorithm for Double-Talk Acoustic Echo Cancellation

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2016年第2期35卷 669-684页

作者： Pu, Kun Zhang, Jiashu Min, Liangwen Southwest Jiaotong Univ Sichuan Prov Key Lab Signal & Informat Proc Chengdu 610031 Peoples R China

Acoustic echo cancellation (AEC) in voiced communication systems is used to eliminate the echo which corrupts the speech signal and reduces the efficiency of signal transmission. Usually, the performance of AEC system based on the adaptive filtering degrades seriously in the presence of speech issued from the near-end speaker (double-talk). In typical AEC scenarios, double-talk detector (DTD) must be added to AEC for improving speech quality. One of the main problems in AEC with DTD is that the DTD errors can result in either large residual echo or distorting the near-end input speech. Considering the strong correlation property of speech signals, this paper presents a novel proportionate decorrelation normalized least-mean-square (PDNLMS) adaptive AEC without DTD for echo cancellation as an interesting alternative to the typical AEC with DTDs. Unlike traditional AEC with a DTD, the proposed PDNLMS uses the difference of near-end speech as the residual error to update adaptive echo channel filter during the periods of double-talk, which can efficiently reduce the double-talk influence on the AEC adaptation process. The experimental results show that not only the proposed PDNLMS without DTD illustrate better stability and faster convergence rate, but it is also of a lower steady-state misalignment and better residual signal than current methods with DTDs at a lower computational cost.

关键词： proportionate algorithm Decorrelation Acoustic echo cancellation Double-talk

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