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Proceedings of the 6th International Conference on Information Technologies and Electrical Engineering

作者： Jinlong Xie School of Information Engineering Wuhan University of Technology China

ISBN: (纸本)9798400708299

adaptive algorithms are used in digital signal processing to reduce noise in audio. adaptive filters play an important role in implementing the idea of adaptive algorithms. The aim of this thesis is to explore the application and performance comparison of adaptive filtering algorithms in audio signal processing through MATLAB simulation analysis. Through two different application scenarios, including audio white noise cancellation and single-frequency white noise cancellation, this paper demonstrates the efficacy of LMS, NLMS, and RLS algorithms in noise cancellation tasks. Meanwhile, the update curves of the filter parameters under different step sizes and the update curves of the filter weights of LMS, NLMS and RLS algorithms under the same step size are compared. When the step sizes of the LMS algorithm and the NLMS algorithm are 0.001 and 0.01, respectively, and the weights of the RLS algorithm are set to 0.99, and the number of filter orders are set to 20, the three algorithms deal with the single-frequency white noise cancellation more stably. The RLS algorithm converges faster the signal is more stable.

关键词： adaptive filtering algorithms LMS NLMS Noise cancellation RLS

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Cramér–Rao Lower Bound of adaptive filtering algorithms for acoustic echo cancellation

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Signal Processing 2026年 238卷

作者： Zongsheng Zheng Ziyuan Shao Yi Yu Lu Lu Shilin Gao College of Electrical Engineering Sichuan University Chengdu 610065 China School of Information and Control Engineering Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang 621010 China College of Electronics and Information Engineering Sichuan University Chengdu 610065 China

Numerous adaptive filtering algorithms have been proposed for acoustic echo cancellation. However, whether the performance of the algorithms approaches the optimal performance or if there has been intentionally overstated remains challenging to evaluate. Fortunately, the Cramér–Rao Lower Bound (CRLB) provides a theoretical minimum variance for any unbiased estimator under given observational data and statistical models. This paper derives the CRLB of adaptive filtering algorithms for acoustic echo cancellation (AEC), in which the generalized Gaussian distribution (GGD) is utilized to model the Gaussian/non-Gaussian background noises. To accelerate the CRLB calculation process, the recursive resolution of the CRLB is presented by using the matrix inversion lemma, and the computational complexity is also analyzed. The derivation results indicate that CRLB for AEC model depends on the acoustic input (i.e., speaker’s voice) and the statistical properties of GGD noise but is unaffected by the channel sparsity. The CRLB derived in this paper can serve as a benchmark to evaluate whether the performance of the adaptive filtering algorithms is optimal and to exclude some adaptive filtering algorithms that deliberately exaggerate their performance.

关键词： Acoustic echo cancellation (AEC) Cramér–Rao Lower Bound (CRLB) Generalized Gaussian distribution (GGD) adaptive filtering algorithms

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A state-of-the-art survey on noise removal in a non-stationary signal using adaptive finite impulse response filtering: challenges, techniques, and applications

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年第4期56卷 885-918页

作者： Yadav, Nilesh Kumar Dhawan, Amit Tiwari, Manish Jha, Sumit Kumar Motilal Nehru Natl Inst Technol Allahabad Dept Elect & Commun Engn Prayagraj 211004 India

An adaptive finite impulse response (FIR) filter is a key technique to remove noise in non-stationary signals. With the rapid development of the various adaptive algorithms, it is both urgent and challenging to comprehensively review the relevant studies. Therefore, this paper thoroughly examined adaptive filters for the existing literature related to adaptive filtering in noise cancellation. The adaptive filter provides a superior approach for the suppression of noise from non-stationary signals. Although there already exist some valuable surveys on adaptive filters, they do not provide a systematic overview of the challenges faced by adaptive filters and lack a careful distinction and comparison of the various adaptive filter techniques in noise cancellation in non-stationary signals. Three key aspects, namely the challenges faced by adaptive filters, the evolutionary paths involved in the adaptive filter algorithm, and the specific application tasks are discussed in detail. Further, the MATLAB simulations have been carried out to evaluate the filtering performance of the adaptive filter.

关键词： adaptive filter adaptive noise cancellation adaptive filtering algorithms performance analysis

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A shrinkage adaptive filtering algorithm with graph filter models

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 703-712页

作者： Shuai, Wei Ni, Hongyu Wu, Jinghai Lin, Zurong Yan, Wenxu Wang, Wenyuan Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China State Grid Shaoxing Power Supply Co Shaoxing Peoples R China

In this study, we focus on an adaptive filtering algorithm that utilizes variable step-size and incorporates graph filter models within the realm of graph signal processing. The algorithm optimizes the step-size by minimizing the energy of the noise-free a posteriori error signal. To extract this noise-free signal from its noisy counterpart, we employ a shrinkage method. Through simulations involving zero-mean i.i.d. Gaussian input signals on a sensor network graph, we demonstrate that our graph-constrained shrinkage least-mean squares (GC-SHLMS) algorithm significantly outperforms traditional algorithms. Specifically, it excels in terms of both convergence speed and steady-state misalignment when compared to the centralized graph-LMS algorithm (GCLMS), the conventional variable-step-size graph-LMS algorithm (GVSSLMS) and the diffusion graph-LMS algorithm (GDLMS).

关键词： adaptive filtering algorithms Graph signal processing Graph filter Shrinkage LMS algorithm

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Robust Shrinkage Affine-Projection Sign adaptive-filtering algorithms for Impulsive Noise Environments

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第13期62卷 3349-3359页

作者： Bhotto, Md Zulfiquar Ali Ahmad, M. Omair Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Ctr Signal Proc & Commun Montreal PQ H3G 1M8 Canada

Two robust affine projection sign (RAPS) algorithms, both of which minimize the mixed norm of l(1) and l(2) of the error signal, are proposed. The direction vector of the RAPS algorithms is obtained from the gradient of an l(1) norm-based objective function, while two related l(2) norm-based minimization problems are solved to obtain the line search of the two RAPS algorithms. The l(1) norm-based direction vector reduces the impact of impulsive noise, whereas the l(2) norm-based line search produces an unbiased solution in the proposed algorithms. In addition, one of the two RAPS algorithms shares the data selective adaptation used in the set-membership (SM) affine projection (SMAP) algorithm. The proposed algorithms are shown to offer a significant improvement in the convergence speed as well as a significant reduction in the steady-state misalignment relative to the pseudo affine projection sign (PAPS) algorithm. In addition, the proposed algorithms offer robust performance with respect to impulsive noise and improved tracking of the unknown system in comparison to that provided by the PAPS and Affine projection sign (APS) algorithms. These features of the proposed algorithms are demonstrated using simulation results in system-identification and echo-cancellation applications.

关键词： adaptive filtering algorithms affine-projection algorithms set-membership adaptive filters

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Model-fusion-based online glucose concentration predictions in people with type 1 diabetes

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CONTROL ENGINEERING PRACTICE 2018年 71卷 129-141页

作者： Yu, Xia Turksoy, Kamuran Rashid, Mudassir Feng, Jianyuan Hobbs, Nicole Hajizadeh, Iman Samadi, Sediqeh Sevil, Mert Lazaro, Caterina Maloney, Zacharie Littlejohn, Elizabeth Quinn, Laurie Cinar, Ali Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China IIT Dept Biomed Engn Chicago IL 60616 USA IIT Dept Chem & Biol Engn Chicago IL 60616 USA IIT Dept Elect & Comp Engn Chicago IL 60616 USA Univ Chicago Kovler Diabet Ctr Dept Pediat & Med Chicago IL 60637 USA Univ Illinois Coll Nursing Dept Biobehav Hlth Sci Chicago IL 60612 USA

Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and prediction accuracy of the data-based fusion filters, especially in the case of limited data availability. The model fusion framework may be used in the development of an AP system for glucose regulation in patients with T1D. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： adaptive filtering algorithms Model fusion strategy Online glucose prediction Type 1 diabetes

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Exact analysis of the least-mean-square algorithm with coloured measurement noise

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ELECTRONICS LETTERS 2018年第24期54卷 1401-1402页

作者： Lara, Pedro Olinto, Karen da S. Petraglia, Felipe R. Haddad, Diego B. CEFET RJ Rio De Janeiro RJ Brazil Univ Fed Rio de Janeiro Rio de Janeiro RJ Brazil

In general, theoretical analyses of adaptive filtering algorithms employ statistical approximations in order to render the derivations tractable. Among such hypotheses, the statistical independence between the current adaptive coefficients and past input vectors is a very popular one. Unfortunately, this simplification gives rise to discrepancies with respect to empirical results, especially for large values of the step-size parameter. In this Letter, this issue is overcome by the usage of an exact expectation analysis (i.e. a stochastic model that does not employ the above-mentioned independence assumption) of the least-mean-squares adaptive algorithm. The authors analysis is also generalised in order to address the common case of coloured additive noise, an issue that is so far missing from the literature. The accuracy of the advanced model is verified through simulations.

关键词： least mean squares methods adaptive filters filtering theory stochastic processes statistical independence current adaptive coefficients input vectors simplification empirical results step-size parameter exact expectation analysis stochastic model above-mentioned independence assumption least-mean-squares authors analysis coloured additive noise exact analysis least-mean-square algorithm coloured measurement noise general analyses theoretical analyses adaptive filtering algorithms statistical approximations derivations tractable

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Efficient variable step-size diffusion normalised least-mean-square algorithm

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ELECTRONICS LETTERS 2015年第5期51卷 395-397页

作者： Jung, Sang Mok Seo, Ji-Hye Park, PooGyeon Pohang Univ Sci & Technol Pohang Gyungbuk South Korea

An efficient variable step-size diffusion normalised least-mean-square algorithm is proposed via a mean-square deviation (MSD) analysis for the distributed estimation. The proposed algorithm has two distinguishing features for computational efficiency. In the adaptation step, an intermittent adaptation rule that dynamically adjusts an update interval is proposed to reduce the redundant updates. In the diffusion step, instead of the existing combination rules, a selection rule is proposed to select the intermediate estimate of the most reliable node among its neighbour nodes for the estimate at each node. Moreover, to achieve both fast convergence rate and low steady-state error, a variable step size is obtained by minimising the MSD.

关键词： least mean squares methods filtering theory variable step-size diffusion normalised least-mean-square algorithm mean-square deviation analysis distributed estimation steady-state error MSD diffusion step fast convergence rate steady-state error adaptive filtering algorithms

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Single channel fetal ECG recovery using sparse redundant representations

Single channel fetal ECG recovery using sparse redundant rep...

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10th International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Ciocoiu, Iulian B. Gheorghe Asachi Tech Univ Iasi Fac Elect Telecommun & Informat Technol Iasi Romania

ISBN: (纸本)9781457702013

A novel approach to fetal ECG signal recovery using a single abdominal record is proposed. Following transforming the original ECG waveform into a 2D array, we apply an image denoising procedure based on sparse representations over overcomplete dictionaries obtained by the K-SVD algorithm. A modified version of this algorithm using an L-1-norm fidelity term is shown to improve the performances over the classical L-2-norm approach. Experimental results indicate that the quality of the recovered fetal ECG signals is comparable to those employing multi-channel recordings and Independent Components Analysis or adaptive filtering algorithms.

关键词： 2D array Algorithm design and analysis Databases Dictionaries ECG waveform Electrocardiography Image denoising K-SVD algorithm L1-norm fidelity term L2-norm approach Noise Signal processing algorithms adaptive filtering algorithms adaptive filters electrocardiography fetal ECG signal recovery fetal ECG signals image denoising image denoising procedure independent component analysis independent components analysis medical signal processing multichannel recordings obstetrics overcomplete dictionary signal representation single abdominal record single channel fetal ECG recovery singular value decomposition sparse redundant representations sparse representations waveform analysis

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adaptive filtering algorithms designed using control Liapunov functions

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IEEE SIGNAL PROCESSING LETTERS 2006年第4期13卷 224-227页

作者： Diene, O Bhaya, A Univ Fed Rio de Janeiro Dept Elect Engn NACAD COPPE BR-21945970 Rio De Janeiro Brazil

The standard conjugate gradient (CG) method uses orthogonality of the residues to simplify the formulas for the parameters necessary for convergence. In adaptive filtering, the sample-by-sample update of the correlation matrix and the cross-correlation vector causes a loss of the residue orthogonality in a modified online algorithm, which, in turn, results in loss of convergence and an increase of the filter quadratic mean error. This letter extends a recently proposed control Liapunov function analysis of the CG method viewed as a dynamic system in the standard feedback configuration to the case of adaptive filtering.

关键词： adaptive equalizer adaptive filtering algorithms control Liapunov function (CLF) iterative methods linear prediction system identification

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