Efficient μ-law improved proportionate affine projection algorithm for echo cancellation

Yang, Jun; Sobelman, Gerald E.

doi:10.1049/el.2010.7937

Cited by 21 publications

(15 citation statements)

References 3 publications

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“…The segment approximation has been also used for other algorithms [13]. It can be noticed that the weight update equation of SNNP-RZA-MFxPAP algorithm is more similar now with the equation update for the NNP-ZA-MFxPAP algorithm after the use of segment linear function.…”

Section: The Proposed Algorithmsmentioning

confidence: 99%

Low-complexity non-uniform penalized affine projection algorithms for active noise control

Albu

Wang

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper describes new algorithms that incorporates the non-uniform norm constraint into the zeroattracting and reweighted modified filtered-x affine projection or pseudo affine projection algorithms for active noise control. The simulations indicate that the proposed algorithms can obtain better performance for primary and secondary paths with various sparseness levels with insignificant numerical complexity increase. It is also shown that the version using a linear function instead of the reweighted term leads to the best results, particularly for combinations of sparse or semi-sparse primary and secondary paths.

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Section: The Proposed Algorithmsmentioning

confidence: 99%

Low-complexity non-uniform penalized affine projection algorithms for active noise control

Albu

Wang

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Several variants of APA have been proposed to incorporate the sparsity nature into the algorithm. The famous among them being proportionate APA and its variants [15], [20],set membership APA [19], partial update APA [8]. These algorithms work by varying either the step size or the number of update coefficients or the projection order, as per the magnitude of the impulse response so that improved performance can be achieved with the increase in sparseness .However they perform well only in sparse environment and their performance degrades when the sparsity level is decreased [4].…”

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confidence: 99%

Steady State Mean Square Analysis of Convex Combination of ZA-APA and APA for Acoustic Echo Cancellation

Radhika

Sivashanmugam²

2015

Advances in Intelligent Systems and Computing

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This paper proposes a new approach for the cancellation of acoustic echo in a loud speaker enclosed microphone system. The acoustic echo is often sparse in nature and the level of sparseness also changes with time. The input in such applications, is speech which is highly correlated. Thus there is a requirement of an adaptive filter which can work in both sparse and non sparse environment and perform well even for correlated inputs. We present convex combination of conventional affine projection algorithm (APA) with small step size and projection order and zero attraction APA (ZA-APA) for echo cancellation. Steady state excess mean square (EMSE) error analysis revealed that the proposed algorithm converges to conventional APA in non sparse environment and to ZA-APA in case of sparse environment and in semi sparse condition, the steady state EMSE value is at least same as the lesser filter's steady state error or even smaller depending on the value of the constant chosen. Simulation is performed in the context of acoustic echo cancellation and the validity of the proposed algorithm is proved from the simulation results.

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“…By taking into account the history of the proportionate factors, a memorised improved proportionate APA (MeIPAPA) has been developed to reduce the computational complexity [4]. Recently, m-law MeIPAPA (MMIPAPA) has been proposed to further improve the overall convergence rate [5]. To meet the conflicting requirements of the fast convergence rate and the low steady-state misalignment, a variable explicit regularisation algorithm for the APA (VR-APA) has been designed [6].…”

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confidence: 99%

“…MMIPAPA [5]: A linear time invariant system can be modelled as d(n) = X T (n)h + v(n), where h = [h 0 , h 1 , . .…”

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Variable regularisation efficient μ-law improved proportionate affine projection algorithm for sparse system identification

et al. 2012

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For sparse system identification, a m-law memorised improved proportionate affine projection algorithm (MMIPAPA) can achieve faster convergence rate than the standard affine projection algorithm. However, the MMIPAPA with constant regularisation parameter requires a tradeoff between fast convergence speed and low steady-state error. To address the problem, proposed are two kinds of variable non-identity regularisation matrices for the MMIPAPA with a negligible additional computational cost and a stability condition for the step-size choice. Simulation results show the good misalignment performance of the proposed algorithms for both coloured and speech input.Introduction: System identification is one of the most important applications of adaptive filtering algorithms. For sparse systems, proportionate normal least-mean-square (PNLMS) has been designed to improve the convergence speed by updating each filter coefficient in proportion to the magnitude of its estimate [1]. Following this approach, many proportionate-type algorithms have been derived, such as improved PNLMS (IPNLMS) [2] and m-law PNLMS (MuPNLMS) [3]. However, the affine projection algorithm (APA) is preferred over least-mean-square (LMS) owing to its faster convergence speed, particularly for coloured input. By taking into account the history of the proportionate factors, a memorised improved proportionate APA (MeIPAPA) has been developed to reduce the computational complexity [4]. Recently, m-law MeIPAPA (MMIPAPA) has been proposed to further improve the overall convergence rate [5]. To meet the conflicting requirements of the fast convergence rate and the low steady-state misalignment, a variable explicit regularisation algorithm for the APA (VR-APA) has been designed [6]. The regularisation matrix for the VR-APA is a variable scalar weighted identity matrix, derived by letting the L 2 -norm of the a posterior error vector equal that of the system noise vector. In this Letter, however, to equate the component of the a posterior error energy vector with the system noise variance instead, we propose two kinds of variable non-identity regularisation matrices for the MMIPAPA, termed the VR-MMIPAPA and the improved VR-MMIPAPA. The proposed algorithms are expected to further improve the overall performance for sparse system identification with a negligible additional computational cost.

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Efficient μ-law improved proportionate affine projection algorithm for echo cancellation

Cited by 21 publications

References 3 publications

Low-complexity non-uniform penalized affine projection algorithms for active noise control

Low-complexity non-uniform penalized affine projection algorithms for active noise control

Steady State Mean Square Analysis of Convex Combination of ZA-APA and APA for Acoustic Echo Cancellation

Variable regularisation efficient μ-law improved proportionate affine projection algorithm for sparse system identification

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