Echo Canceller Integrating Diverse Adaptive Filtering Algorithms
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It is a significant problem how we select step-size of adaptive filtering algorithm so as to solve effectively the conflict between convergence speed and steady misalignment in echo cancellation. This paper presents a novel echo cancellation scheme which integrates a diverse set of adaptive filtering methods and different choices of step-size, such that their advantages can be integrated and the improved performance can be achieved. In the proposed scheme, the performance of different methods are analyzed, thus being able to keep selecting the best methods for output. An empiri-cal study on the integration of four methods, namely, LMS, NLMS, PNLMS and IPNLMS, indicates that the proposed scheme is effective in terms of both convergence speed and good steadiness.Keywords:
Echo (communications protocol)
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In this work we introduce a variable step-size normalized LMS algorithm for adaptive echo cancellation in a FIRstructure. In the proposed scheme, the step-size adjustment is controlled by using the square of the cross-correlationbetween the squared output error and the adaptive filter output. The proposed algorithm (that we call VSSSC aftervariable step size based on the squared cross-correlation) was evaluated using white noise and speech signals.Simulation results show that our proposal achieves better performance than similar algorithms in single and doubletalk. The proposed algorithm can be used in a number of applications such as in echo reduction for long-haul voicecommunications.
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Proportionate adaptive filters, such as the improved proportionate normalized least-mean-square (IPNLMS) algorithm, have been proposed for echo cancellation as an interesting alternative to the normalized least-mean-square (NLMS) filter. Proportionate schemes offer improved performance when the echo path is sparse, but are still subject to some compromises. In this paper, we study how combination schemes, where the output of two independent adaptive filters are adaptively mixed together, can be used to increase IPNLMS robustness to channels with different degrees of sparsity, as well as to alleviate the rate of convergence vs steady-state misadjustment tradeoff imposed by the selection of the step size. The advantages of these combined filters are illustrated in several echo cancellation scenarios.
Robustness
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This paper introduces the working principles of noise cancellation with adaptive filtering,expounds the selection of algorithms,and illustrates the reasons of selecting LMS algorithm and makes simulation.
Active Noise Control
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In this paper, the set-theoretic based adaptive filtering task is studied for the case where the input signal is nonstationary and may assume relatively small values. Such a scenario is often faced in practice, with a notable application that of echo cancellation. It turns out that very small input values can trigger undesirable behaviour of the algorithm leading to severe performance fluctuations. The source of this malfunction is geometrically investigated and a solution complying with the set-theoretic philosophy is proposed. The new algorithm is evaluated in realistic echo-cancellation scenarios and compared with state–of–the–art methods for echo cancellation such as the IPNLMS and IPAPA algorithms.
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In this paper, the problem of echo cancellation in long acoustic impulse responses (AIRs) is highlighted. Three of the mostly-used recent NLMS-based sparse adaptive filtering algorithms are presented; and their performances in the context of acoustic echo cancellation (AEC) are studied and compared. The algorithms of interest include the improved proportionate normalized least mean square (IPNLMS), its sparseness-controlled (SC) upgrade (SC-IPNLMS) as well as the so-called variable-step-size reweighted zero-attractor NLMS (VSS-RZA-NLMS) which is based on the compressive sensing (CS) framework. Series of simulations were carried out both in synthetic and real different-sparseness long acoustic impulse responses with stationary and non-stationary inputs in order to effectively analyze, evaluate and compare the strengths and the weaknesses of these algorithms in terms of convergence speed, steady-state performance and computational complexity.
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An adaptive algorithm for reducing the hardware complexity is presented. This paper proposes a modified LMS algorithm for the adaptive system and analyzes its convergence characteristics mathematically. An objective of the proposed algorithm is to reduce the hardware complexity. In order to test the performances, it is applied to the echo canceller, and a program is described. The results from simulations show that the echo canceller adopting the proposed algorithm achieves almost the same performances as one adopting the LMS algorithm. If an echo canceller is implemented with this algorithm, its computation quantities are reduced to half as many as that implemented with the normal LMS algorithm, without so much degradation of performance.
Echo (communications protocol)
Adaptive algorithm
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Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellation. Besides a comprehensive review of the basic proportionate-type algorithms, we also present some of the latest developments in the field and propose some new solutions for further performance improvement, e.g., variable step-size versions and novel proportionate-type affine projection algorithms. An experimental study is also provided in order to compare many sparse adaptive filters in different echo cancellation scenarios.
Echo (communications protocol)
Finite impulse response
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A study is made of the performance of a class of adaptive data-driven echo cancellers (DDECs). The authors first investigate the interrelationship in a unified framework among the system equations of various data-driven echo cancellers. As a result, they obtain a new DDEC algorithm that consists of only one real-valued adaptive structure. The authors analyze and compare the convergence behavior of DDECs. They analyze their complexities when the least-mean-square (LMS) algorithm and the frequency-domain block LMS algorithm are used for adjusting the canceller weights. The results show that the echo canceller structure realized in the frequency domain has advantages in convergence rate and in implementation complexity as compared to existing DDEC structures.< >
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Adaptive algorithm
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Adaptive filter is the key point of IP echo cancellation.This paper discussed and researched the principle of acoustic echo cancellation,the basic algorithm and implementation process of some of the key problems,then proposed an improved LMS algorithm which is apply to the computer software toimplement,namely NLMS algorithm,and through the experimental results proved that it can improve the convergence performance,ameliorate call quality.
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In this paper we propose a new adaptive FIR filter algorithm for echo cancellation applications. The proposed algorithm differs from the conventional one by the construction of input signals on an optimal way using the D-optimal experiment design. The advantages in using the proposed adaptive FIR echo canceller are demonstrated through simulation results.
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Finite impulse response
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