A robust echo canceler for acoustic environments

Doherty, J.F.; Porayath, R.

doi:10.1109/82.580846

Cited by 45 publications

(18 citation statements)

References 8 publications

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“…However, the proof of its algorithm is not presented in Ref. [12]. In the following, we will show that the mean value of the filtering weight is close to the solution of generalized Wiener equation when iterative times increases infinitely.…”

Section: The Structure and Convergence Analysis Of The New Algorithmmentioning

confidence: 91%

“…where, μ is a real number which can be adjusted; δ is substituted by H ( ) ( ) n n X U [12]. However, the proof of its algorithm is not presented in Ref.…”

Section: The Structure and Convergence Analysis Of The New Algorithmmentioning

confidence: 98%

“…[11]. However, it performs as LMS algorithm for uncorrelated signal inputs [12]. A variable step size decorrelation VSDLMS algorithm by injecting pseudorandom code is presented in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…A variable step size decorrelation VSDLMS algorithm by injecting pseudorandom code is presented in Ref. [12], which has good performance versus increasing system cost.…”

Section: Introductionmentioning

confidence: 99%

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Study of a new fast adaptive filtering algorithm

Wang

Zhang

Yang

et al. 2006

Front. Electr. Electron. Eng. China

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A new fast adaptive filtering algorithm was presented by using the correlations between the signal's former and latter sampling times. The proof of the new algorithm was also presented, which showed that its optimal weight vector was the solution of generalized Wiener equation. The new algorithm was of simple structure, fast convergence, and less stable maladjustment. It can handle many signals including both uncorrelated signal and strong correlation signal. However, its computational complexity was comparable to that of the normalized least-mean-square (NLMS) algorithm. Simulation results show that for uncorrelated signals, the stable maladjustment of the proposed algorithm is less than that of the VS-NLMS algorithm, and its convergence is comparable to that of the algorithm proposed in references but faster than that of L.E-LMS algorithm. For strong correlation signal, its performance is superior to those of the NLMS algorithm and DCR-LMS algorithm.

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Section: The Structure and Convergence Analysis Of The New Algorithmmentioning

confidence: 91%

“…where, μ is a real number which can be adjusted; δ is substituted by H ( ) ( ) n n X U [12]. However, the proof of its algorithm is not presented in Ref.…”

Section: The Structure and Convergence Analysis Of The New Algorithmmentioning

confidence: 98%

“…[11]. However, it performs as LMS algorithm for uncorrelated signal inputs [12]. A variable step size decorrelation VSDLMS algorithm by injecting pseudorandom code is presented in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…A variable step size decorrelation VSDLMS algorithm by injecting pseudorandom code is presented in Ref. [12], which has good performance versus increasing system cost.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Study of a new fast adaptive filtering algorithm

Wang

Zhang

Yang

et al. 2006

Front. Electr. Electron. Eng. China

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“…That is, the spectrally overlapping DSSS system is designed by inserting an additional channel in the middle of two neighboring channels, and then frequency shift filter (FSF) is introduced for channel separation and signals extraction. As a result, this system's capacity can increase several-times [1][2][3][4][5][6][7]. Studies on large narrowband interference suppression can be seen in other literatures [8][9][10][11][12][13][14][15], which apply the cyclostationary property of signals in wireless communi-cation systems with narrowband interference.…”

Section: Introductionmentioning

confidence: 99%

Separating cyclostationary signals from spectrally overlapping interference

Liu

Guo

et al. 2006

Front. Electr. Electron. Eng. China

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This paper studies an algorithm about separating spectrally overlapping signals using the cyclostationary properties of signals. On the basis of direct sequence spread system (DSSS), frequency shift filter is added into the receiver of the communication system. Although the structure of frequency shift filter is more complicated than the time-domain filter, it uses both time correlations and frequency spectrum correlations so it can achieve better performances on separating the overlapping signals. After the analysis of cyclostationary characteristic and frequency spectrum correlation, the structure of the frequency shift filter can be gained. Then, a self-adaptive algorithm is utilized for the purpose of achieving optimum multidimensional tap weights of frequency shift components. The simulation results indicate that this method can efficiently separate overlapping signals, and its error rate is lower than the time-domain filter or DSSS system by two orders of magnitude on the condition that high-power interference is added into the system.

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A variable step‐size control of adaptation for acoustic echo cancellation filters

Pahor¹,

Carini²

2002

Trans Emerging Tel Tech

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This paper presents a low-complexity variable step-size control for acoustic echo cancellation filters. The proposed control method is based on two supplementary adaptive filters with low memory length. The first supplementary filter is used to control the double-talk situations while the second supplementary filter is employed to detect variations of the echo-path impulse response. The variable step-size here derived is independent from the echo coupling loss and from the reverberation time and thus it is suitable for both hands-free and handset terminals

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A robust echo canceler for acoustic environments

Cited by 45 publications

References 8 publications

Study of a new fast adaptive filtering algorithm

Study of a new fast adaptive filtering algorithm

Separating cyclostationary signals from spectrally overlapping interference

A variable step‐size control of adaptation for acoustic echo cancellation filters

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