Kernel-based identification of Hammerstein systems for nonlinear acoustic echo-cancellation

Vaerenbergh, Steven Van; Azpicueta-Ruiz, Luis A.

doi:10.1109/icassp.2014.6854300

Cited by 16 publications

(18 citation statements)

References 20 publications

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“…Other proposed structures include even mirror Fourier nonlinear filters [10], block-based Wiener-Hammerstein models [11], functional link adaptive filters [12], etc. Other alternatives were mentioned in [13].…”

Section: Introductionmentioning

confidence: 97%

“…It was shown that the input-output relationship was learnt with fewer data and less computational resources [13]. The method from [13] involves many inversions of very large matrices, therefore the numerical complexity is high. We propose to use iterative methods in order to reduce the computational load associated with matrices inversions.…”

Section: Introductionmentioning

confidence: 99%

“…They still have a high computational complexity, although it is possible to limit the filter grow for online kernel methods [15]. In [13] the modeling of the echo path as a Hammerstein system and kernel-based identification algorithm for this model were proposed. It was shown that the input-output relationship was learnt with fewer data and less computational resources [13].…”

Section: Introductionmentioning

confidence: 99%

“…In [13] the modeling of the echo path as a Hammerstein system and kernel-based identification algorithm for this model were proposed. It was shown that the input-output relationship was learnt with fewer data and less computational resources [13]. The method from [13] involves many inversions of very large matrices, therefore the numerical complexity is high.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

New iterative kernel algorithms for nonlinear acoustic echo cancellation

Albu

Nishikawa

2015

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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Recently, a nonlinear acoustic echo cancellation algorithm based on the framework of kernel methods has been proposed by modeling the echo path as a Hammerstein system. However, it requires a large amount of computation to be implemented. In this paper, we propose to use iterative methods for solving linear systems in order to reduce the numerical complexity of identifying the nonlinear and linear parts of the echo path. Also we investigate the effect on performance of the parameters of the methods for both iterative batch and online versions. Simulation results confirm the complexity reduction and good performance of the proposed methods for clipping nonlinearity and room impulse response variation.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New iterative kernel algorithms for nonlinear acoustic echo cancellation

Albu

Nishikawa

2015

2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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“…In [12], a functional link adaptive filter was introduced to model nonlinear acoustic echo path based on a Hammerstein structure. Vaernbergh et al used kernel-based identification of the Hammerstein system [13].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear acoustic echo cancellation using an adaptive Hammerstein block structure based on a generalized basis function

Onur¹,

Hacıoğlu²

2017

Turk J Elec Eng & Comp Sci

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Abstract:We investigated adaptive algorithms for a Hammerstein block structure in which a static nonlinear block and dynamic linear block are cascaded. The approach considered here is to use generalized orthonormal basis functions in a Hammerstein block structure by using fixed pole filter banks. We applied the normalized least mean square approach to the developed adaptive algorithm in order to acquire Hammerstein block structure parameters. Performance comparison of the proposed approach was investigated considering convergence speed and parametric complexity for acoustic echo cancellation application. The results indicated that in the developed algorithm along with appropriate selection of fixed poles, the algorithm convergences faster and less parametric complexity is provided when compared to direct adaptive Hammerstein algorithms with IIR and FIR linear blocks.

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Nonlinearity recovery by standard and aggregative orthogonal series algorithms

Śliwiński

Wachel

Lagosz

2018

Appl Stoch Models Bus & Ind

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In this paper, the problem of nonlinearity recovery in Hammerstein systems is considered. Two algorithms are presented: the first is a standard orthogonal series algorithm, whereas the other, ie, the aggregative one, exploits the convex programming approach. The finite sample size properties of both approaches are examined, compared, and illustrated in a numerical experiment. The aggregative algorithm performs better when the number of measurements is comparable to the number of parameters; however, it also imposes additional smoothness restrictions on the recovered nonlinearities.

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Kernel-based identification of Hammerstein systems for nonlinear acoustic echo-cancellation

Cited by 16 publications

References 20 publications

New iterative kernel algorithms for nonlinear acoustic echo cancellation

New iterative kernel algorithms for nonlinear acoustic echo cancellation

Nonlinear acoustic echo cancellation using an adaptive Hammerstein block structure based on a generalized basis function

Nonlinearity recovery by standard and aggregative orthogonal series algorithms

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