A widely linear model for stereophonic acoustic echo cancellation

Stanciu, Cristian; Benesty, Jacob; Paleologu, Constantin; Gänsler, Tomas; Ciochin, Silviu

doi:10.1016/j.sigpro.2012.08.017

Cited by 49 publications

(48 citation statements)

References 9 publications

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“…Hence, we seek to employ the proportionate update idea in the MFE framework. To this end, for the stochastic cost function J 2 ðd t ; x t ; wÞ 9ðd t À x T t wÞ 4 , we obtain the PNLMF algorithm [18], given by…”

Section: Proportionate Update Approachmentioning

confidence: 99%

“…Many signal processing problems such as noise removal, e.g., recent works [1][2][3], echo cancellation, e.g., recent works [4][5][6][7], and channel equalization, e.g., recent works [8,9], can be formulated in the general system-identification framework depicted in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

“…(3) We introduce the KPNLMMN and the stable-KPNLMF algorithms achieving better trade-off in terms of the transient and steady-state performance under certain settings. (4) We provide a complete performance analysis, i.e., the transient and the steady state analyses; and analyze the tracking performance in a non-stationary environment. (5) We demonstrate the improved convergence performance of the proposed algorithms through several numerical examples under different scenarios.…”

Section: Introductionmentioning

confidence: 99%

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The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

et al. 2015

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a b s t r a c tWe propose novel adaptive filtering algorithms based on the mean-fourth error objective while providing further improvements on the convergence performance through proportionate update. We exploit the sparsity of the system in the mean-fourth error framework through the proportionate normalized least mean fourth (PNLMF) algorithm. In order to broaden the applicability of the PNLMF algorithm to dispersive (non-sparse) systems, we introduce the Krylov-proportionate normalized least mean fourth (KPNLMF) algorithm using the Krylov subspace projection technique. We propose the Krylov-proportionate normalized least mean mixed norm (KPNLMMN) algorithm combining the mean-square and mean-fourth error objectives in order to enhance the performance of the constituent filters. Additionally, we propose the stable-PNLMF and stable-KPNLMF algorithms overcoming the stability issues induced due to the usage of the mean fourth error framework. Finally, we provide a complete performance analysis, i.e., the transient and the steadystate analyses, for the proportionate update based algorithms, e.g., the PNLMF, the KPNLMF algorithms and their variants; and analyze their tracking performance in a non-stationary environment. Through the numerical examples, we demonstrate the match of the theoretical and ensemble averaged results and show the superior performance of the introduced algorithms in different scenarios.

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Section: Proportionate Update Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

et al. 2015

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“…During the research of signal cancellation, more attention has been paid to radar interference signal or clutter [7][8][9], acoustic signal [10][11][12], linear frequency modulation pulse compression signal [13,14]. LFM pulse compression signal is utilized worldwide in modern radar systems.…”

Section: Introductionmentioning

confidence: 99%

Active cancellation analysis based on the radar detection probability

Wang

Huang

2015

Aerospace Science and Technology

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In this paper, a cancelling system for linear and three nonlinear frequency modulated signals is proposed. According to the RCS (Radar Cross Section) characteristic of target, amplitude and phase modulation are made to acquire the signal having the same frequency as the echo pulse but with the opposite phase. This signal cancels the radar echo signal. The basic theory of active cancellation stealth is introduced. Based on two kinds of radar target fluctuation models (Swerling I and Swerling III), the formulas of radar detection probability are given. The influence of amplitude, phase and frequency error on the radar detection probability have been discussed. Simulation results show that the technology of active cancellation stealth is feasible in theory, it could reduce the radar detection probability.

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“…In our recent work [1], [6], we proposed a different approach for the SAEC problem, by using the widely linear (WL) model [7]. In fact, the classical two-input/two-output system with real random variables was recast as a single-input/singleoutput system with complex random variables.…”

Section: Introductionmentioning

confidence: 99%

A Kalman filter for stereophonic acoustic echo cancellation

Paleologu

Benesty

Grant

et al. 2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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The stereophonic acoustic echo cancellation (SAEC) scheme was recently recast by using the widely linear (WL) model, i.e., as a single-input/single-output system with complex random variables. In this paper, we present a Kalman filter with individual control factors in the context of the WL model for SAEC. As a specific feature, this algorithm uses a different level of uncertainty for each coefficient of the filter. Simulation results indicate that the proposed algorithm outperforms the recursive least-squares (RLS) algorithm, which is usually considered as the benchmark for SAEC.

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A widely linear model for stereophonic acoustic echo cancellation

Cited by 49 publications

References 9 publications

The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

The Krylov-proportionate normalized least mean fourth approach: Formulation and performance analysis

Active cancellation analysis based on the radar detection probability

A Kalman filter for stereophonic acoustic echo cancellation

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