Adaptation of a memoryless preprocessor for nonlinear acoustic echo cancelling

Stenger, Alexander; Kellermann, Walter

doi:10.1016/s0165-1684(00)00085-2

Cited by 132 publications

(85 citation statements)

References 8 publications

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“…The derived bound on step-sizes is only of theoretical importance as in general, Equation (27) cannot be verified in a practical situation. Similar theoretical results can be found in [7].…”

Section: Stability Analysismentioning

confidence: 98%

“…Hammerstein filters can accurately model many real-world systems and, as a consequence, they have been successfully used in various applications of engineering [26][27][28][29]. Due to its simplicity and efficiency, the mean square error (MSE) criterion has been widely applied in Hammerstein adaptive filtering [30].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1 shows the structure of a Hammerstein adaptive filter under MCC criterion, where the filter consists of a polynomial memoryless nonlinearity followed by a linear FIR filter. This structure has been commonly used in Hammerstein adaptive filtering [8,9,27]. As shown in Figure 1, under the MCC criterion, the parameters of the linear and nonlinear parts are adjusted to maximize the correntropy between the model output and desired response.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion

Peng

Chen

et al. 2015

Entropy

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Abstract:The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE) criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable) noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm.

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Section: Stability Analysismentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion

Peng

Chen

et al. 2015

Entropy

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“…Birkett and Goubran proposed a neural network-based approach with a cascade structure [8]. Stenger and Kellerman reported a new faster perspective for estimating high order nonlinearity that does not need an extra reference microphone [9]. A NARMAX structure method was also proposed for nonlinear echo cancellation that is a general parametric model but needs a pre-identification procedure [10].…”

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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“…In such cases, the performance of a linear acoustic canceller degrades. A common approach is to consider nonlinear models [10][11]. Sometimes, in the case of the nonlinear distortions, SVFs can achieve a better system identification than a linear one can provide, but at the price of a much higher complexity.…”

Section: The Algorithmsmentioning

confidence: 99%