Modified NLMF adaptation of Volterra filters used for nonlinear acoustic echo cancellation

Contan, Cristian; Kirei, Botond Sandor; ̧opa, Marina Dana T

doi:10.1016/j.sigpro.2012.11.017

Cited by 20 publications

(10 citation statements)

References 8 publications

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“…Noticing that ∂ w.l w t will be different from zero only when l = t, thus, adopting the Kronecker delta function [38], ∂ w,l w t = δ t,l . Finally, substituting (44) into (42), the stochastic gradient is obtained…”

Section: B Normalized Least Mean Fourth Algorithm Based On Geometricmentioning

confidence: 99%

“…From Fig.10 and Fig.11, we can know that GA-NLMF requires less iterations to track the actual signal than GA-NLMS, and GA-NLMF tracks more accurately than GA-NLMS algorithms. From [42], [43], we know that NLMF has a faster convergence than NLMS algorithm, and NLMF is a higher-order statistic, making the weight update coefficient more robust, therefore the performance of NLMF is better than NLMS algorithm. Besides, the GA theory extends the NLMS and NLMF algorithm in GA space, allowes them to process multidimensional signals in a holistic way, and remains the relationship among each component of the multidimensional signal.…”

Section: ) Ga-nlmf and Ga-lmk Algorithmmentioning

confidence: 99%

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A Normalized Adaptive Filtering Algorithm Based on Geometric Algebra

Wang

Liang

et al. 2020

IEEE Access

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In this paper, we extend the original Normalized Least Mean Fourth (NLMF) and Normalized Least Mean Square (NLMS) adaptive filtering algorithms into Geometric Algebra (GA) space to enable them to process multidimensional signals. We redefine the cost functions and propose the GA based NLMF and NLMS algorithms (GA-NLMF & GA-NLMS). We take full advantage of the ability of GA to represent multidimensional signals in GA space. GA-NLMS minimizes the cost function of the normalized mean square of the error signal, and remain stable as the input signal of the filter increases. GA-NLMS has fast convergence rate but higher steady-state error. The GA-NLMF algorithm minimizes the cost function of the normalized mean fourth of the error signal. Simulation results show that our proposed GA-NLMS adaptive filtering algorithm outperforms original NLMS algorithm in terms of convergence rate and steady-state error, and GA-NLMF outperforms both NLMF and GA-NLMS algorithms. GA-NLMF has faster convergence rate and lower steady state error, which is proved in the experiments.

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Section: B Normalized Least Mean Fourth Algorithm Based On Geometricmentioning

confidence: 99%

Section: ) Ga-nlmf and Ga-lmk Algorithmmentioning

confidence: 99%

A Normalized Adaptive Filtering Algorithm Based on Geometric Algebra

Wang

Liang

et al. 2020

IEEE Access

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“…The input signal is an AR(1) with a pole at 0.5. The performance measure is the echo return loss enhancement (ERLE), which is given as [33] ERLE(n) = 10log 10 E{d 2 (n)} E{e 2 (n)} [dB].…”

Section: Acoustic Echo Cancelation Systemmentioning

confidence: 99%

A novel sign adaptation scheme for convex combination of two adaptive filters

Zhao

et al. 2015

AEU - International Journal of Electronics and Communications

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“…Hence, the familiar method of NLMS is not appropriate for AEC, owing to its rate of convergence for SEP [5]. The best way to simulate convergence is by SAF technique depending on a significant structure of sampling similar to SAFs [11][12][13]. Further, NSAF is presented that revealed better performance, along with its complexity which is nearer to that of the NLMS algorithm [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Robust Normalized Subband Adaptive Filter For Acoustic Noise And Echo Cancellation Systems

Samuyelu¹,

Kumar²

2019

AMA_B

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In modern decades, a major problem like echo in recent communication network can be solved by AEC. Even though AEC can be measured as a characteristic significance of identifying system, that is recognizing the path of echo; current teleconferencing and automatic telephones systems enforce numerous restrictions on traditional AFs. The initial limitation occurs when the signals given at input are the signals of speech that are frequently highly colored more willingly when distinguished with white noise; moreover, subsequent one is the impulse response in which the path of echo is sparse and extensive. It further designates that many coefficients are either zero or equivalent to zero. This paper implements a D-MVS-SNSAF technique for identifying the echo cancellation systems by adopting NSAF technique. Here, the amount of transitions in the input/output signals was measured for deriving the polynomial order from two sets of audio signals as input. Further, the proposed method is distinguished with conventional algorithms like NSAF, SS-NSAF, VS-NSAF, VS-SNSAF and MVS-SNSAF and enhancement in the implemented method is proved.

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Modified NLMF adaptation of Volterra filters used for nonlinear acoustic echo cancellation

Cited by 20 publications

References 8 publications

A Normalized Adaptive Filtering Algorithm Based on Geometric Algebra

A Normalized Adaptive Filtering Algorithm Based on Geometric Algebra

A novel sign adaptation scheme for convex combination of two adaptive filters

Robust Normalized Subband Adaptive Filter For Acoustic Noise And Echo Cancellation Systems

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