A variable-step-size NLMS algorithm using statistics of channel response

Shi, Kun; Ma, Xiaoli

doi:10.1016/j.sigpro.2010.01.015

Cited by 20 publications

(14 citation statements)

References 7 publications

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“…The simulation results show that the proposed VSS-SPU-NLMS has fast convergence speed and low steady-state MSD features. Also this algorithm has close performance to VSS-NLMS in [21]. Good performance can be seen for proposed VSS-SPU-NLMS.…”

Section: Resultsmentioning

confidence: 59%

“…But the computational complexity of VSS-SPU-APA is lower than VSS-APA. Figure 5 compares the MSD curves of SPU-NLMS, VSS-NLMS in [21], proposed VSS-SPU-NLMS, ES [19] and GNGD algorithms [16]. The parameters B, and S were set to 4, and 2 respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Similar to [21], we assume that the entries of X(n) and v(n) are zero-mean independent and identically distributed (i.i.d) sequence with variance σ 2 x and σ 2 v , respectively; w(n), X(n) and v(n) are mutually independent. Therefore, we obtain from (51),…”

Section: -3 Variablementioning

confidence: 99%

“…In [21] a new variable-step-size control was proposed for the NLMS algorithm. In this algorithm, the step-size vector with different values for each filter coefficient was used.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we extend the approach in [21] to the APA, SPU-NLMS, SPU-APA, and SR-APA and four novel VSS adaptive filter algorithms are established. These algorithms are computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

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A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

Abadi

Arani

2011

EURASIP J. Adv. Signal Process.

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This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal stepsize vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.

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Section: Resultsmentioning

confidence: 59%

Section: Resultsmentioning

confidence: 99%

Section: -3 Variablementioning

confidence: 99%

“…In [21] a new variable-step-size control was proposed for the NLMS algorithm. In this algorithm, the step-size vector with different values for each filter coefficient was used.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

Abadi

Arani

2011

EURASIP J. Adv. Signal Process.

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A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

Djendi

2017

Adaptive Control & Signal

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This study addresses the problem of speech quality enhancement by adaptive and nonadaptive filtering algorithms. The well-known two-microphone forward blind source separation (TM-FBSS) structure has been largely studied in the literature. Several two-microphone algorithms combined with TM-FBSS have been recently proposed. In this study, we propose 2 contributions: In the first, a new two-microphone Gauss-Seidel pseudo affine projection (TM-GSPAP) algorithm is combined with TM-FBSS. In the second, we propose to use the new TM-GSPAP algorithm in speech enhancement. Furthermore, we show the efficiency of the proposed TM-GSPAP algorithm in speech enhancement when highly noisy observations are available. To validate the good performances of our algorithm, we have evaluated the adaptive filtering properties in computational complexity and convergence speed performance by system mismatch criteria. A fair comparison with adaptive and nonadaptive noise reduction algorithms are also presented. The adaptive algorithms are the well-known two-microphone normalized least mean square algorithm, and the recently published two-microphone pseudo affine projection algorithm. The nonadaptive algorithms are the one-microphone spectral subtraction and the two-microphone Wiener filter algorithm. We evalute the quality of the output speech signal in each algorithm by several objective and subjective criteria as the segmental signalto-noise ratio, cepstral distance, perceptual evaluation of speech quality, and the mean opinion score. Finally, we validate the superior performances of the proposed algorithm with physically measured signals.

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Variable Step-size LMS Algorithm Based on Hyperbolic Tangent Function

Zhao

2023

Circuits Syst Signal Process

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A variable-step-size NLMS algorithm using statistics of channel response

Cited by 20 publications

References 7 publications

A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

Variable Step-size LMS Algorithm Based on Hyperbolic Tangent Function

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