A Family of Variable Step-Size NLMS Algorithms for Echo Cancellation (original) (raw)

Variable step-size NLMS algorithms designed for echo cancellation

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, 2009

The normalized least-mean-square (NLMS) algorithm is one of the most common choices for echo cancellation. Nevertheless, an NLMS algorithm has to compromise between several performances criteria (e.g., convergence rate versus misadjustment, tracking capabilities versus robustness). Thus, a variable step-size NLMS (VSS-NLMS) algorithm represents a more reliable solution. Recently, several VSS-NLMS algorithms that take into account the existence of the near-end signal (in terms of power estimate) have been proposed with the objective of recovering the near-end signal from the error signal. Since this is the basic goal in echo cancellation, this class of VSS-NLMS algorithms can be very suitable for such an application. The main issue remains the estimation of the near-end signal power, in terms of accuracy and other practical aspects (e.g., available parameters, computational complexity). This paper analyzes different solutions for this problem, making a first unified approach over the performances of this family of VSS-NLMS algorithms.

The performance study of NLMS algorithm for acoustic echo cancellation

2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC), 2017

We provide the performance Study of NLMS Algorithm for audio echo termination. The AEC (Acoustic Echo Cancellation) has been completed based on their convergence rate, MSE (Mean Square Error), steady state ERLE (Echo Return Loss Enhancement) and computational difficulty. The results explain the NLMS (Normalized Least Mean Square) algorithm comes with increased convergence rate whereas controlling minimal computational difficulty. It may be displayed as permitting the attenuation value along with the integer of replication functions; it's the obvious choice useful for the real location AEC system.

Computationally-Efficient DNLMS-Based Adaptive Algorithms for Echo Cancellation Application

Journal of Communications, 2006

This paper investigates the application of the delayed normalized least mean square (DNLMS) algorithm to echo cancellation. In order to reduce the amount of computations, DNLMS is modified by using computationallyefficient techniques including the M-Max algorithm, a Stopand-go (SAG) algorithm, and Power-of-two (POT) quantization. For the SAG algorithm, a new stopping criterion related to the regressor energy is presented. Cumulatively, these modifications lead to reductions in power and/or area. Simulation results and comparisons with the normalized least mean square (NLMS) algorithm are included to show the advantages of the computationally-efficient algorithms.

Variable Step-Size NLMS Algorithm for Under-Modeling Acoustic Echo Cancellation

IEEE Signal Processing Letters, 2000

In acoustic echo cancellation (AEC) applications, where the acoustic echo paths are extremely long, the adaptive filter works most likely in an under-modeling situation. Most of the adaptive algorithms for AEC were derived assuming an exact modeling scenario, so that they do not take into account the under-modeling noise. In this letter, a variable step-size normalized least-mean-square (VSS-NLMS) algorithm suitable for the under-modeling case is proposed. This algorithm does not require any a priori information about the acoustic environment; as a result, it is very robust and easy to control in practice. The simulation results indicate the good performance of the proposed algorithm.

Adaptive echo cancellation using least mean mixed-norm algorithm

IEEE Transactions on Signal Processing, 1997

A novel algorithm for echo cancellation is presented in this work. The algorithm consists of simultaneously applying the least mean square (LMS) algorithm to the near-end section of the echo canceller and the least mean fourth (LMF) algorithm to the far-end section. This new scheme results in a substantial performance improvement over the LMS algorithm and other algorithms.

Improved variable step-size NLMS adaptive filtering algorithm for acoustic echo cancellation

Digital Signal Processing, 2016

Acoustic echo canceller (AEC) is used in communication and teleconferencing systems to reduce undesirable echoes resulting from the coupling between the loudspeaker and the microphone. In this paper, we propose an improved variable step-size normalized least mean square (VSS-NLMS) algorithm for acoustic echo cancellation applications based on adaptive filtering. The steady-state error of the NLMS algorithm with a fixed step-size (FSS-NLMS) is very large for a non-stationary input. Variable step-size (VSS) algorithms can be used to decrease this error. The proposed algorithm, named MESVSS-NLMS (mean error sigmoid VSS-NLMS), combines the generalized sigmoid variable step-size NLMS (GSVSS-NLMS) with the ratio of the estimation error to the mean history of the estimation error values. It is shown from single-talk and double-talk scenarios using speech signals from TIMIT database that the proposed algorithm achieves a better performance, more than 3 dB of attenuation in the misalignment evaluation compared to GSVSS-NLMS, non-parametric VSS-NLMS (NPVSS-NLMS) and standard NLMS algorithms for a non-stationary input in noisy environments.

A Practical Overview of Recursive Least-Squares Algorithms for Echo Cancellation

Due to its fast convergence rate, the recursive least-squares (RLS) algorithm is very popular in many applications of adaptive filtering. However, the computational complexity of this algorithm represents a major limitation in some applications that involve long filters, like echo cancellation. Moreover, the specific features of this application require good tracking capabilities and double-talk robustness for the adaptive algorithm, which further imply an optimization process on its parameters. In the case of most RLS-based algorithms, the performance can be controlled in terms of two main parameters, i.e., the forgetting factor and the regularization term. In this paper, we outline the influence of these parameters on the overall performance of the RLS algorithm and present several solutions to control their behavior, taking into account the specific requirements of echo cancellation application. The resulting variable forgetting factor RLS (VFF-RLS) and variable-regularized RLS (...

Double-talk robust VSS-NLMS algorithm for under-modeling acoustic echo cancellation

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008

Most of the adaptive algorithms used for acoustic echo cancellation (AEC) are designed assuming an exact modeling scenario (i.e., the acoustic echo path and the adaptive filter have the same length) and a single-talk context (i.e., the near-end speech is absent). In real-world AEC applications, the adaptive filter works most likely in an under-modeling situation, i.e., its length is smaller than the length of the acoustic impulse response, so that the under-modeling noise is present. Also, the double-talk case is almost inherent, so that a double-talk detector (DTD) is usually involved. Both aspects influence and limit the algorithm's performance. Taking into account these two practical issues, a double-talk robust variable step size normalized least-meansquare (VSS-NLMS) algorithm is proposed in this paper. This algorithm is nonparametric in the sense that it does not require any information about the acoustic environment, so that it is robust and easy to control in practice.