A low complexity fast converging partial update adaptive algorithm employing variable step-size for acoustic echo cancellation (original) (raw)
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A practical data-reuse adaptive algorithm for acoustic echo cancellation
2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012
There are many strategies to improve the overall performance of the classical adaptive filters. Among these strategies, the data-reuse algorithms aim to improve the convergence rate by reusing the same set of data (i.e., the input and reference signals) several times. Another possibility is to use a variable step size (VSS) to achieve a proper compromise between the convergence rate and misadjustment of the adaptive filter. Nevertheless, both approaches increase the computational complexity. In this paper, we present an efficient data-reuse algorithm, which is the result of a combination between 1) a low-complexity implementation of the data-reuse process and 2) a simple and practical mechanism for controlling the step size. Simulations performed in the context of acoustic echo cancellation indicate the good performance of the proposed approach.
A Variable Step Size for Acoustic Echo Cancellation Using Normalized Sub band Adaptive Filter
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013
Numerous various step size normalized least mean square (VSS-NLMS)Algorithms have been derived to solve the problem of fast convergence rate and low mean square error.Here we find out the ways to control the step size. A normalized subband adaptive filter algorithm uses a fixed and variable step size, which is chosen as a trade-off between the steady-state error and the convergence rate. A variable step size for normalized subband adaptive filter is derived by minimizing the mean-square deviation between the optimal weight vector and the estimated weight vector at each instant of time. The variable step size is presented in terms of error variance. Therefore, we verify thedifferent algorithmseither they are capable of tracking in stationary and non-stationary environments. The results show good tracking ability and low misalignment of the algorithm in system identification. Parameters are tracking, steady state errors, and misalignment, environment, step size.
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.
Partial Update Simplified Fast Transversal Filter Algorithms for Acoustic Echo Cancellation
Traitement du Signal, 2022
Robust algorithms applied in Acoustic Echo Cancellation systems present an excessive calculation load that has to be minimized. In the present paper, we propose two different low complexity fast least squares algorithms, called Partial Update Simplified Fast Transversal Filter (PU-SMFTF) algorithm and Reduced Partial Update Simplified Fast Transversal Filter (RPU-SMFTF) algorithm. The first algorithm reduces the computational complexity in both filtering and prediction parts using the M-Max method for coefficients selection. Moreover, the second algorithm applies the partial update technique on the filtering part, joined to the P-size forward predictor, to get more complexity reduction. The obtained results show a computational complexity reduction from (7L+8) to (L+6M+8) and from (7L+8) to (L+M+4P+17) for the PU-SMFTF algorithm and RPU-SMFTF algorithm, respectively compared to the original Simplified Fast Transversal Filter (SMFTF). Furthermore, experiments picked out in the contex...
Performance comparison of adaptive algorithms applied to acoustic echo cancelling
2003 IEEE International Symposium on Industrial Electronics ( Cat. No.03TH8692), 2003
In this paper, the convergence behaviors of fulliiand and sub-band LMS algorithms are compared for the acoustic echo canceling application. Post-filtering techniques for the reduction of the residual echo and noise are also described. Experimental results with measured speech signals are presented. hidex Ternshands-free telephony, adaptive filtering, filter banks, acoustic echo cancellation, noise reduction. ' This work was partially supported by CNF'q/UFRJ, Brazil.
The problem of acoustic echo is well defined in case of hands-free communication.The presence of large acoustic coupling between the loudspeaker and microphone would produce an echo that causes a reduction in the quality of the communication.The solution to this problem is the elimination of the echo with an echo canceller which increases the speech quality and improves listening experience. In this paper, many prominent work done in relation to acoustic echo cancellation (AEC) is discussed and analysed. The existing AEC algorithms are analysed and compared based on their merits and demerits in a time varying echoed environment. It covers the basic algorithms like least mean square (LMS) , normalized least mean square (NLMS) and recursive least square algorithm as well as their modified versions like variable step size NLMS, fractional LMS, Filtered-x LMS, variable tap-length LMS algorithm, multiple sub-filter (MSF) based algorithms, variable tap-length MSF structures etc. Finally, a judicious comparison is presented towards the end of the paper in order to judge the best AEC algorithm in the present time.
2017
To eliminate acoustic echo, the convergence rate and low residual echo are very important to adaptive echo cancelers. Meanwhile, an affordable computational complexity has to be considered as well. In this paper, we proposed the improved vector space adaptive filter (IVAF)and Improved Vector-space Affine Projection Sign Algorithm (IVAPSA). The proposed can be divided into two phases: offline and online. In the offline phase, IVAF constructs a vector space to incorporate the prior knowledge of adaptive filter coefficients from a wide range of different channel characteristics. Then, in the online phase, the IVAF combines the conventional APSA and IVAPSA algorithms, where IVAPSA computes the filter coefficients based on the vector space obtained in the offline phase. By leveraging the constructed vector space, the proposed IVAF is able to fast converge and achieve a better echo return loss enhancement performance. Moreover, the computational complexity is less than a comparable work.
Time distributed update of the NLMS algorithm coefficients for Acoustic Echo Cancellers
Proceedings of the 5th …, 2004
Hands free operation is a standard feature of many fixed and mobile telephone sets. In hands free telephony, echoes appear at the far end, because of the open-air acoustic path between the loudspeaker and microphone at the near end. This paper presents an efficient realization of an Acoustic Echo Cancellation (AEC) algorithm, as well as its implementation structure and performance aspects. The algorithm is based on the Least Mean Square (LMS) family of algorithms . The innovative features of this algorithm provide significant gains in terms of computational complexity and signal quality. The proposed method is based on the segmentary update of the coefficients of the N-LMS filter. It is proved that the convergence behavior of the introduced algorithm is similar to the N-LMS; it is also proved that segmentary update provides similar quality to the original N-LMS, while significantly reducing complexity. The algorithm can be easily implemented and it is particularly suited for simultaneous echo cancellation on multiple voice channels. It is hence appropriate for echo cancellation implementation in access devices as well as in central office equipment. The results presented in this paper have been collected from the algorithm's implementation on a standard commercial DSP.
Fast Recursive Least Squares Algorithm for Acoustic Echo Cancellation Application
2007
Adaptive filtering is used in a wide range of applications including echo cancellation, noise cancellation and equalization. In these applications, the environment in which the adaptive filter operates is often non-stationary. For satisfactory performance under non-stationary conditions, an adaptive filtering is required to follow the statistical variations of the environment. Tracking analysis provides insight into the ability of an adaptive filtering algorithm to track the changes in surrounding environment. The tracking behavior of an algorithm is quite different from its convergences behavior. While convergence is a transient phenomenon, tracking is a steady-state phenomenon. Over the last decade a class of equivalent algorithms such as the Normalized Least Mean Squares algorithm (NLMS) and the Fast Recursive Least Squares algorithm (FRLS) has been developed to accelerate the convergence speed. In acoustic echo cancellation context, we propose in this paper to use numerically stable Fast Recursive Least Squares algorithm to improve the quality and the intelligibility of the enhanced speech.