A combination of two NLMS filters in an adaptive line enhancer

Trump, Tonu

doi:10.1109/icdsp.2011.6004882

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…The combination of two (or more) adaptive filters has been studied mostly from the perspective of combining two filters, where one would grant faster convergence and the other a better steady-state performance [7], [8], [11]. Traditionally the outputs of the two adaptive filters are combined according to 1 .…”

Section: Combination Of Filtersmentioning

confidence: 99%

An affine combination of adaptive filters for channels with different sparsity levels

Butsenko

Trump

2016

Telfor

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Abstract-In this paper we present an affine combination strategy for two adaptive filters. One filter is designed to handle sparse impulse responses and the other one performs better if impulse response is dispersive. Filter outputs are combined using an adaptive mixing parameter and the resulting output shows a better performance than each of the combining filters separately. We also demonstrate that affine combination results in faster convergence than a convex combination of two adaptive filters.

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Section: Combination Of Filtersmentioning

confidence: 99%

An affine combination of adaptive filters for channels with different sparsity levels

Butsenko

Trump

2016

Telfor

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“…To address this difficulty, one useful approach is to rely on combinations of adaptive structures. Combinations of adaptive schemes have been studied in several works [7]- [21] and have been applied to a variety of applications such as the characterization of signal modality [22], acoustic echo cancellation [23]- [26], adaptive line enhancement [27], array beamforming [28], [29], and active noise control [30], [31].…”

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confidence: 99%

Combinations of Adaptive Filters

Arenas-García,

Azpicueta-Ruiz,

Silva

et al. 2021

Preprint

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Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancellation [1], array beamforming [2], channel equalization [3], and to more recent sensor network applications in surveillance, target localization and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5].Ranging from the simple Least-Mean-Squares (LMS) to sophisticated state-space algorithms, significant research has been carried out over the last 50 years to develop effective adaptive algorithms, in an attempt to improve their general properties in terms of convergence, tracking ability, steady-state misadjustment, robustness, or computational cost [6]. Many design procedures and theoretical models have been developed, and many novel adaptive structures are continually proposed with the objective of improving filter behavior with respect to well-known performance tradeoffs (such as convergence rate versus steady-state performance), or incorporating available a priori knowledge into the learning mechanisms of the filters (e.g., to enforce sparsity).

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“…These combined schemes are introduced to improve robustness when several kinds of adverse scenario conditions can impair the filter performance, and to facilitate the selection of filter parameters, alleviating the different trade-offs inherit to adaptive filters, for instance the well-know speed of convergence vs steady-state misadjustment compromise [26]. Combination of adaptive filters have been successfully employed in different signal processing applications, including system identification [26], [27], signal modality characterization [28], array beamforming [29], [30], adaptive line enhancement [31], and acoustic applications, such as AEC [32]- [35] and ANC [23], [36], [37].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

Fúster

Diego

Azpicueta-Ruiz

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Room equalization has become essential for sound reproduction systems to provide the listener with the desired acoustical sensation. Recently, adaptive filters have been proposed as an effective tool in the core of these systems. In this context, this paper introduces different novel schemes based on the combination of adaptive filters idea: a versatile and flexible approach that permits obtaining adaptive schemes combining the capabilities of several independent adaptive filters.In this way, we have investigated the advantages of a scheme called combination of block-based adaptive filters which allows a blockwise combination splitting the adaptive filters into nonoverlapping blocks. This idea was previously applied to the plant identification problem, but has to be properly modified to obtain a suitable behavior in the equalization application. Moreover, we propose a scheme with the aim of further improving the equalization performance using the a priori knowledge of the energy distribution of the optimal inverse filter, where the block filters are chosen to fit with the coefficients energy distribution. Furthermore, the biased block-based filter is also introduced as a particular case of the combination scheme, especially suited for low signal-to-noise ratios (SNRs) or sparse scenarios. Although the combined schemes can be employed with any kind of adaptive filter, we employ the filtered-x improved proportionate normalized least mean square (Fx-IPNLMS) algorithm as basis of the proposed algorithms, allowing to introduce a novel combination scheme based on partitioned block schemes where different blocks of the adaptive filter use different parameter settings. Several experiments are included to evaluate the proposed algorithms in terms of convergence speed and steady-state behavior for different degrees of sparseness and SNRs.

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A combination of two NLMS filters in an adaptive line enhancer

Cited by 8 publications

References 13 publications

An affine combination of adaptive filters for channels with different sparsity levels

An affine combination of adaptive filters for channels with different sparsity levels

Combinations of Adaptive Filters

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

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