Affine Projection Versoria Algorithm for Robust Adaptive Echo Cancellation in Hands-Free Voice Communications

Huang, Fuyi; Zhang, Jiashu; Zhang, Sheng

doi:10.1109/tvt.2018.2877457

Cited by 54 publications

(9 citation statements)

References 49 publications

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“…Finally, in order to verify the effectiveness of the proposed NR-RDR-GMCC algorithm, some related algorithms, such as NR-DR-GMCC, GB-GMCC [ 19 ], affine projection GMCC (AP-GMCC) [ 20 ], AP sign algorithm (APSA) [ 29 ], AP Versoria (APV) [ 30 ] and MCC-APA [ 31 ], are considered in this part. For all algorithms, the smooth parameter or regularization parameter

; for all GMCC based algorithms,

; other parameter settings are experimentally tested so that the algorithms have similar initial convergence rates.…”

Section: Simulation Resultsmentioning

confidence: 99%

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

Zhao

Qiao

et al. 2022

Entropy

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For system identification under impulsive-noise environments, the gradient-based generalized maximum correntropy criterion (GB-GMCC) algorithm can achieve a desirable filtering performance. However, the gradient method only uses the information of the first-order derivative, and the corresponding stagnation point of the method can be a maximum point, a minimum point or a saddle point, and thus the gradient method may not always be a good selection. Furthermore, GB-GMCC merely uses the current input signal to update the weight vector; facing the highly correlated input signal, the convergence rate of GB-GMCC will be dramatically damaged. To overcome these problems, based on the Newton recursion method and the data-reusing method, this paper proposes a robust adaptive filtering algorithm, which is called the Newton recursion-based data-reusing GMCC (NR-DR-GMCC). On the one hand, based on the Newton recursion method, NR-DR-GMCC can use the information of the second-order derivative to update the weight vector. On the other hand, by using the data-reusing method, our proposal uses the information of the latest M input vectors to improve the convergence performance of GB-GMCC. In addition, to further enhance the filtering performance of NR-DR-GMCC, a random strategy can be used to extract more information from the past M input vectors, and thus we obtain an enhanced NR-DR-GMCC algorithm, which is called the Newton recursion-based random data-reusing GMCC (NR-RDR-GMCC) algorithm. Compared with existing algorithms, simulation results under system identification and acoustic echo cancellation are conducted and validate that NR-RDR-GMCC can provide a better filtering performance in terms of filtering accuracy and convergence rate.

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; for all GMCC based algorithms,

; other parameter settings are experimentally tested so that the algorithms have similar initial convergence rates.…”

Section: Simulation Resultsmentioning

confidence: 99%

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

Zhao

Qiao

et al. 2022

Entropy

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“…Like the LAF algorithm, the LNAF algorithm is developed uses a Lawson norm [24], [25] to take full advantage of the error information to improve system performance for impulsive scenarios. To construct a robust cost function for LNAF algorithm, the Lawson-norm function integrated with AP scheme [2], [23] is used and given by…”

Section: Proposed Lnaf Algorithmmentioning

confidence: 99%

“…Additionally, it augments the convergence in comparison with the basic AP algorithm when we deal with impulsive interferences. To further enhance its performance, an affine-projection-Versoria (APV) algorithm was reported in [2], which introduces datareusing into Versoria-cost function to improve filter performance. With the benefit combination of the above superiorities, the APV algorithm gains stability for suppressing large outliers and accelerates convergence with correlated signal inputs.…”

Section: Introductionmentioning

confidence: 99%

Lawson-Norm Based Adaptive Filter for Channel Estimation and in-Car Echo Cancellation

Li¹

2022

Preprint

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<div>We proposed a Lawson-norm-based adaptive estimation algorithm within the affine-project theory framework and give a name of Lawson-norm adaptive filter (LNAF) algorithm. The LNAF algorithm is derived, analyzed, and simulated for echo cancellation when background noise is impulsive, which is realized and implemented via using Lawson norm of past errors to take a sliding window on cost function to speed up convergence and achieve robustness for impulsive noises for colored signal. Simulations based on measured data are used for in-car echo cancellation and channel estimation to verify the LNAF algorithm's performance with different inputs, which prove that the LNAF algorithm is superior to the popular AP algorithms under differnt impulsive interferences.</div><div><br></div>

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“…In the point estimation of the smooth region, because the gray value of the pixel in the neighborhood is similar, the corresponding weight of the pixel which is not polluted by noise or the pollution is not serious is larger, and the weight of the isolated point seriously polluted by noise will be relatively small, so as to smooth the noise. The 33  smoothing filter shown in Figure 3 is used in this paper [42]. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.…”

Section: B Algorithm Basic Process 1) Weighted Mean Filter Principlementioning

confidence: 99%

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

et al. 2021

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The image will be contaminated by noise during the imaging process, which severely degrades the image quality. It is necessary to filter the collected image. With the increasing amount of image data, the traditional single-processor or multiprocessor computing equipment has been unable to meet the requirements of real-time data processing. In this paper, the computational model of weighted mean filtering and the characteristics of high performance computer architecture are studied. An efficient hierarchical image weighted mean filtering parallel algorithm for Open Computing Language (OpenCL) is designed and implemented, which can fully express the parallelism of the computing model. The parallel algorithm takes full account of the characteristics of image discrete convolution computing and the multilayer logic architecture of high performance computer, deeply excavates the parallelism of the computing platform and computing model, and realizes the efficient task mapping from computing model to computing resources. The model is implemented in parallel with the two levels of work-group and workitem. The experimental results show that compared with the serial algorithm based on CPU, the parallel algorithm based on Open Multi-Processing (OpenMP) and the parallel algorithm based on Compute Unified Device Architecture (CUDA), the parallel algorithm of weighted mean filtering achieves 20.88 times, 18.52 times and 1.26 times acceleration ratio on the NVIDIA GPU computing platform based on OpenCL architecture, respectively. It realizes better computing performance and runs on different Graphic Processing Unit (GPU) computing platforms, and has good portability and scalability.INDEX TERMS weighted mean filtering; Gaussian noise; Graphic Processing Unit (GPU); Open Computing Language (OpenCL); parallel algorithm.

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Affine Projection Versoria Algorithm for Robust Adaptive Echo Cancellation in Hands-Free Voice Communications

Cited by 54 publications

References 49 publications

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

Lawson-Norm Based Adaptive Filter for Channel Estimation and in-Car Echo Cancellation

A Parallel Algorithm of Image Mean Filtering Based on OpenCL

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