A new adaptive filtering algorithm for stereophonic acoustic echo cancellation

Messini, Merouane; Djendi, Mohamed

doi:10.1016/j.apacoust.2018.11.033

Cited by 11 publications

(5 citation statements)

References 20 publications

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“…Figure 1 depicts the misalignments of the cyclic and leading DCD algorithms. A system matrix R is designed with small condition numbers in the interval [2,5]. It is seen that the cyclic DCD algorithm has a slightly slower convergence than the leading DCD algorithm.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Solving the problems in a wide range of signal processing applications is equivalent to getting the solution of a linear least squares (LS) problem [1]. These applications include adaptive antenna array applications [2], multi-user detection [3], multiple-input multiple-output (MIMO) detection [4], echo cancellation [5], equalization [6], and system identification [1,[7][8][9]. If the channel information is known, zero forcing (ZF) algorithm and minimum mean-square error (MMSE) algorithm are popular to be used in these applications.…”

Section: Introductionmentioning

confidence: 99%

“…Misalignments of the DCD algorithms in the system with small condition numbers in the range of[2,5]; U = 64, M b = 15, Q = 512.…”

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confidence: 99%

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Improved Convergence Speed of a DCD-Based Algorithm for Sparse Solutions

Quan

2020

Algorithms

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To solve a system of equations that needs few updates, such as sparse systems, the leading dichotomous coordinate descent (DCD) algorithm is better than the cyclic DCD algorithm because of its fast speed of convergence. In the case of sparse systems requiring a large number of updates, the cyclic DCD algorithm converges faster and has a lower error level than the leading DCD algorithm. However, the leading DCD algorithm has a faster convergence speed in the initial updates. In this paper, we propose a combination of leading and cyclic DCD iterations, the leading-cyclic DCD algorithm, to improve the convergence speed of the cyclic DCD algorithm. The proposed algorithm involves two steps. First, by properly selecting the number of updates of the solution vector used in the leading DCD algorithm, a solution is obtained from the leading DCD algorithm. Second, taking the output of the leading DCD algorithm as the initial values, an improved soft output is generated by the cyclic DCD algorithm with a large number of iterations. Numerical results demonstrate that when the solution sparsity γ is in the interval [ 1 / 8 , 6 / 8 ] , the proposed leading-cyclic DCD algorithm outperforms both the existing cyclic and leading DCD algorithms for all iterations.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Convergence Speed of a DCD-Based Algorithm for Sparse Solutions

Quan

2020

Algorithms

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“…i is the output of the first hidden layer and O (3) h is the output of the second hidden layer. The outputs of the output layer are shown as equation (15) and (16).…”

Section: Netmentioning

confidence: 99%

“…Software compensation divides into two ways: modern filter and machine learning. Frequently-used modern filters include adaptive filtering algorithm [12]- [15] and Kalman filter [16], [17]. The adaptive filtering based on the Least Mean Square (LMS) is generally suitable for on-line identification of the system and it needs a reference signal.…”

Section: Introductionmentioning

confidence: 99%

Extending Velocity Sensor Bandwidth by Compensating Temperature Dependency Based on BP Neural Network

et al. 2019

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A compensation method for a magnetoelectric velocity sensor (MVS) is always necessary, which can lower the resonance frequency of the measuring system and subsequently extend the measuring bandwidth. In this paper, a novel compensation method is proposed based on the BP neural network under the TensorFlow architecture. Comparing with the existing methods, the new method does not depend upon an accurate model of the MVS any more, whose parameters are badly influenced by the temperature. The dynamic compensator is connected with the sensor. The BP neural network algorithm is used to identify compensation parameters. The dynamic compensator works at state of the optimum parameter all the time to compensate the dynamic performance of MVS by training the weights and thresholds of the neural network. The experiment results show that velocity measurement deviation is within ±5% error band by the dynamic compensator, which can reduce the measurement deviation caused by the variation of temperature and improve the measurement accuracy. The bandwidth can be as low as 0.28Hz. The dynamic compensator is superior to Random Forests and RBF Neutral Network in implement in FPGA/CPLD. Its' accuracy is superior to zero-pole compensation method. This method leads a new way to weaken the temperature variation characteristics of the velocity sensor and improve the measurement performance. INDEX TERMS Velocity sensor, bandwidth expansion, temperature dependence, BP neural network.

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