Acoustic Echo Cancellation Applications Research Articles

Robust Normalized Subband Adaptive Filter Algorithm Based on Logarithmic and Total Least Squares for Correlated Input Signals and Impulsive Noise

The total least squares normalized subband adaptive filter (TLS-NSAF) algorithm proposed in recent years has shown excellent performance in processing the errors-in-variables (EIV) model of correlated input signals. However, when the system is Interferenced by the impulsive noise, the convergence of TLS-NSAF algorithm will be seriously deteriorated. To address this problem, this paper improves the TLS-NSAF algorithm by using the logarithmic function, and proposes a robust NSAF algorithm based on logarithmic and total least squares method (RNSAF-LTLS). The algorithm performs well in an environment where input signal is a correlated input signal and output signal contains impulsive noise. In addition, there are few studies in the existing literature that apply the TLS method to subbands, and there are few performance analyses associated with it. To address this problem, this paper analyzes the local stability, derives the step size that guarantees the stability of the RNSAF-LTLS algorithm, and calculates the steady-state mean squared deviation (S-MSD). Finally, the RNSAF-LTLS algorithm is verified in system identification (SI) and acoustic echo cancellation (AEC) applications. The simulation results prove the superiority of the proposed algorithm and the correctness of the theoretical analysis.

Bias-compensated sign subband adaptive filtering algorithm with individual-weighting-factors: Performance analysis and improvement

This paper analyzes the statistical behavior of the bias-compensated sign subband adaptive filtering algorithm with individual-weighting-factors (BC-IWF-SSAF) under errors-in-variables (EIV) model. The theoretical expressions for transient and steady-state behaviors of BC-IWF-SSAF in the mean and mean-square senses are derived through utilizing some reasonable assumptions and Price's theorem. Furthermore, the stability ranges of algorithm are provided. To accomplish performance enhancement, the variable step-size version (VSS) of BC-IWF-SSAF, called VSS-BC-IWF-SSAF algorithm, is developed based on the transient model via minimizing the mean-square deviation (MSD) at every iteration to tackle the contradictory requirements of quick convergence rate and high filtering accuracy caused by fixed step-size (FSS). Finally, Monte-Carlo simulations under system identification and acoustic echo cancellation applications certificate the effectiveness of statistical analysis and variable step-size strategy.

Time-Variance Aware Dynamic Kernel Generation for Real-Time Acoustic Echo Cancellation

Time-variant factors including dynamic delay and varying echo path often occur in real-world acoustic echo cancellation (AEC) applications. Current end-to-end deep neural network (DNN) based methods usually model the time-variant components implicitly and can hardly handle the unpredictable time-variance in real-time AEC. To explicitly capture the time-variant components, we propose a dynamic kernel generation (DKG) module that can be introduced as a learnable plug-in to a DNN-based end-to-end pipeline. Specifically, the DKG module generates a convolutional kernel regarding to each input audio frame, so that the DNN model is able to dynamically adjust its weights according to the input signal during inference. Experimental results verify that DKG module improves the AEC performance of the model under time-variant scenarios, especially in the double-talk cases.

Combined-Sample Multiband-Structured Subband Filtering Algorithms

This paper introduces two combined-sample multiband-structured subband adaptive filters (MSAFs). In the design, an adaptive convex combination scheme of two self-reliant multi-sampled MSAF (MS-MSAF) with different sampled periods is firstly developed, which leads to the so-called CTMS-MSAF algorithm. Secondly, based on an adaptive filter, the combined-sample MS-MSAF (CMS-MSAF) algorithm is proposed via designing a time-varying sampled period, which possesses lower computational complexity than the former. Then, the convergence behaviors of the CTMS-MSAF and CMS-MSAF algorithms are investigated using standard mean-square deviation analysis. Finally, the simulation study in the system identification and acoustic echo cancellation applications shows that at the same steady-state error, the CMS-MSAF method provides a faster convergence rate than the improved convex combination of two MSAFs, combined-step-size MSAF and CTMS-MSAF algorithms.

Sparsity-aware SSAF algorithm with individual weighting factors: Performance analysis and improvements in acoustic echo cancellation

In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms.

The wavelet transform-domain adaptive filter for nonlinear acoustic echo cancellation

The usage of low-quality components in communicating devices introduces acoustic nonlinearity. The presence of nonlinearity creates challenges in noise cancellation applications, especially the acoustic echo cancellation (AEC) that requires an adaptive filter of a very high order. However, the functional link adaptive filter (FLAF) algorithm models the acoustic nonlinearity efficiently but shows slow convergence performance due to a very high filter order. To improve the convergence performance of the FLAF, the wavelet transform-domain FLAF (WTD-FLAF) is proposed for nonlinear AEC (NAEC) applications. The convergence rate is improved by decomposing a higher-order adaptive filter into smaller-order subfilters. The convergence speed improvement is gained at the expense of increased computational complexity. A low complexity version of the WTD-FLAF, named as selective update WTD-FLAF (SU-WTD-FLAF) algorithm, is also presented. The SU-WTD-FLAF algorithm is based on the selective coefficient update approach. Computer simulations demonstrate that the convergence performance of the proposed algorithms outperforms the standard FLAF.

Robust filtering employing bias compensated M‐estimate affine‐projection‐like algorithm

This Letter presents a bias compensated M-estimate affine-projection-like algorithm for robust adaptive filtering in case of noisy input signal and impulsive noise interference. In order to mitigate the effect of bias introduced due to noisy input signal, a bias compensation vector is derived along with robust cost function minimisation employing M-estimate and accordingly the weight update learning process of the adaptive filter has been improved. In conjunction with this, a new input noise variance estimation method is introduced using the median filter. The proposed algorithm is evaluated for acoustic echo cancellation application and compared with other existing methods. Simulation results demonstrate its effectiveness in terms of enhanced convergence and better steady-state performance in case of impulsive environment and input signal contaminated with noise.

Low-Complexity Decorrelation NLMS Algorithms: Performance Analysis and AEC Application

In the traditional decorrelation normalized least-mean-square (D-NLMS) algorithm, high computational complexity is mainly caused by finding the decorrelated-vector. To address this issue, this article proposes a low-complexity implementation approach, which cleverly utilizes the periodic update of the decorrelation parameters and delay characteristics of the decorrelated-vector. We firstly develop two low-complexity decorrelation algorithms, (i) fast D-NLMS (FD-NLMS) and (ii) approximate FD-NLMS (AFD-NLMS) which is an approximate version of the first algorithm with even smaller computational requirement. Theoretical performance of the FD-NLMS scheme is also derived. To further obtain low steady-state error in the acoustic echo cancellation (AEC) application, separated-decorrelation AEC structure and robust step-size schemes are designed, resulting in two improved algorithms, namely, fast separated-decorrelation NLMS (FSD-NLMS) and approximate FSD-NLMS (AFSD-NLMS). Finally, extensive simulation study on system identification and AEC is undertaken to verify the efficiency of the proposed methods.

A new simplified fast transversal filter algorithm based on subband approach (SSFTF) for acoustic echo cancellation

This paper addresses the problem of acoustic echo cancellation (AEC) by adaptive filtering algorithms with low complexity. In this paper, we propose a subband implementation of the simplified fast transversal filter (SFTF) algorithm. The proposed subband SFTF (SSFTF) is derived for acoustic echo cancellation (AEC) application. The efficient proposed subband decomposition leads to an improvement of the convergence speed performance even in situation when very non-stationary signal is present at the input. The proposed SSFTF algorithm has shown robustness in the background noise presence case. The complexity and comparison of the proposed SSFTF algorithm with state of the art algorithms have shown its superiority in terms of convergence speed performance that is evaluated by the mean square error (MSE) and system mismatch (SM) objective criteria. Intensive experiments have been conducted on the new algorithm to show all the performance with different scenarios in AEC application. All the obtained results have shown the best performances and efficiency of the new SSFTF algorithm.

Robust Set-Membership Normalized Subband Adaptive Filtering Algorithms and Their Application to Acoustic Echo Cancellation

This paper presents a family of robust set-membership normalized subband adaptive filtering (RSM-NSAF) algorithms for acoustic echo cancellation (AEC). By using a new robust set-membership error bound, the RSM-NSAF algorithm obtains improved robustness against impulsive noises and decreased steady-state misalignment relative to the conventional set-membership NSAF (SM-NSAF) algorithm. To exploit the sparsity of the impulse response, the $L_{0}$ norm constraint robust set-membership NSAF ( $L_{0}$ -RSM-NSAF), robust set-membership improved proportionate NSAF (RSM-IPNSAF), and $L_{0}$ norm constraint robust set-membership improved proportionate NSAF ( $L_{0}$ -RSM-IPNSAF) algorithms are derived by minimizing a differentiable cost function that utilizes the Riemannian distance between the updated and previous weight vectors as well as the $L_{0}$ norm of the weighted updated weight vector. Simulations in AEC application confirm the improvements of the proposed algorithms in performance.

Nonlinear acoustic echo cancellation using an adaptive Hammerstein block structure based on a generalized basis function

We investigated adaptive algorithms for a Hammerstein block structure in which a static nonlinear block and dynamic linear block are cascaded. The approach considered here is to use generalized orthonormal basis functions in a Hammerstein block structure by using fixed pole filter banks. We applied the normalized least mean square approach to the developed adaptive algorithm in order to acquire Hammerstein block structure parameters. Performance comparison of the proposed approach was investigated considering convergence speed and parametric complexity for acoustic echo cancellation application. The results indicated that in the developed algorithm along with appropriate selection of fixed poles, the algorithm convergences faster and less parametric complexity is provided when compared to direct adaptive Hammerstein algorithms with IIR and FIR linear blocks.

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

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 Variable Mode QR Decomposition Adaptive Filtering Algorithm for Acoustic Echo Cancellation

This paper studies a new QR decomposition adaptive filtering algorithm for acoustic echo cancellation (AEC). Based on the p-TA-QR-LS algorithm [1] and an efficient voice activity detection technique, the proposed algorithm can distinguish the significant and insignificant input data periods. The resultant variable mode p-TA-QR-LS algorithm can work between two modes (p=1and N) and is thus suitable for AEC application where reusing significant input data can enhance convergence and the computation cost can be saved when the input is relatively weak.

A Variable Mode QR Decomposition Adaptive Filtering Algorithm for Acoustic Echo Cancellation

This paper studies a new QR decomposition adaptive filtering algorithm for acoustic echo cancellation (AEC). Based on the p-TA-QR-LS algorithm [ and an efficient voice activity detection technique, the proposed algorithm can distinguish the significant and insignificant input data periods. The resultant variable mode p-TA-QR-LS algorithm can work between two modes (p=1and N) and is thus suitable for AEC application where reusing significant input data can enhance convergence and the computation cost can be saved when the input is relatively weak.

APA with Evolving Order and Variable Regularization for Echo Cancellation

Recently APA has become one of most popular algorithms in application of Acoustic Echo Cancellation. Because of the contradictory factors of convergence rate and steady-state misalignment, a new algorithm by the behavior of associating variable regularization and evolving order has been proposed in this paper. Despite of the conventional assumption that the a posteriori error is zero, we take the statistical characteristic of the noise into consideration during the adaptation process. Exact and approximate formulations for the optimal regularization factor are derived. Numerical simulation results show that the proposed algorithm improves the performance of the APA in terms of its faster convergence rate and lower steady-state misalignment compared to existing variable regularization APA and evolving order APA, respectively. Meanwhile it can be seen that near-end speech signal has been restored more effectively

An Improved Subband Adaptive Filter for Acoustic Echo Cancellation Application

Subband adaptive filters (SAFs) play an important role in digital signal processing field. We propose an improved subband adaptive filter which can be used to remove echo in telephone communication. The presented structure is based on polyphase decomposition of the filter to be integrated with Kalman filtering strategy. Compared to the Normalized Subband adaptive filter(NSAF) algorithm, the novel algorithm exhibits faster convergence when Kalman filtering algorithm is utilized for coefficient updating. Furthermore, with an increased number of subbands in the filter, the convergence rate improves considerably. The efficacy of the proposed algorithm is examined and validated by mathematical analysis and simulation.

A robust variable step-size affine projection algorithm

We present a robust variable step-size affine projection algorithm (RVSS-APA) using a recently introduced new framework for designing robust adaptive filters. The algorithm is the result of minimizing the square norm of the a posteriori error vector subject to a time-dependent constraint on the norm of the filter update. The RVSS-APA is then successfully tested in different environments for system identification and acoustic echo cancellation applications.

Fast and Robust Adaptation of DFT-Domain Volterra Filters in Diagonal Coordinates Using Iterated Coefficient Updates

This paper presents a novel data-reusing technique for implementing adaptive discrete Fourier transform (DFT) domain Volterra filters in diagonal coordinates having arbitrary nonlinear order. In general, a major drawback of such nonlinear filters is the large number of parameters. Thus, a weak excitation of higher-order kernels results in a slow convergence for system identification tasks. In order to exploit the available innovation of the signals more effectively, each data frame is processed for a specified number of iterations in an overlap-save scheme, thereby enhancing the convergence performance. Due to inherent recursions of the repeated filtering and updating steps, this concept also lends itself to a fast algorithm, requiring only a moderate increase in algorithmic complexity over a baseline implementation. A detailed comparison of the required number of multiplications is given for several different algorithm versions. Despite the relatively simple concept, the outlined iteration algorithm exhibits significant gains in both adaptation speed and steady-state convergence. This is demonstrated by various experiments for both stationary noise and speech input considering an application of nonlinear acoustic echo cancellation. Finally, investigations on the tracking behavior for time-variant nonlinearities and robustness against misdetection of double-talk situations confirm the promising benefits and good trade-off of the proposed iterated coefficient updates Volterra filters approach.

A new fast Newton‐type adaptive filtering algorithm for stereophonic acoustic echo cancellation (SAEC)

AbstractThis paper addresses the problem of acoustic echo cancellation. We propose a new version of the fast Newton transversal filter algorithm for stereophonic acoustic echo cancellation applications. This algorithm can be viewed as an efficient implementation of the extended two‐channel fast transversal filter algorithm. Moreover, it fits naturally within the frame of the fast version of the recursive least‐squares (RLS) algorithm, applied to the two‐channel case. To stabilize the proposed two‐channel algorithm, we have adapted and then applied a new numerical stabilization technique that has been proposed recently. The computational complexity of the proposed two‐channel algorithm is less than half the complexity of the fastest two‐channel RLS versions and very close to that of the two‐channel normalized least mean squares algorithm when its predicting part length is chosen to be small. Simulation results and comparisons in term of complexities, convergence speed and tracking with the two‐channel algorithms are presented. Copyright © 2009 John Wiley & Sons, Ltd.

An optimum algorithm for adaptive filtering on acoustic echo cancellation using TMS320C6713 DSP

In this study, it is aimed to enhance the intelligibility of speech by canceling out the echo noise. For this purpose, the data transfer software, which is necessary for real time processing of voice signals, and the adaptive filtering algorithm software for the application of acoustic echo cancellation have been developed. An algorithm has been proposed for the determination of optimum adaptation rate (@m) for the least-mean-square (LMS) adaptation algorithm that is used in the adaptive filter. The effectiveness of our optimum @m value determination algorithm was demonstrated on a single direction voice conference application with one speaker. In this study, we used a DSP card (TMS320C6713), a Laptop computer, an amplifier, a loudspeaker and two microphones in two applications. In the first application, two microphones were placed close to the loudspeaker, while in the other application, one microphone was placed close to loudspeaker and speech trial was implemented in the far-end microphone. Output of the adaptive filter was observed for @m values of 0, 0.1, 100 and optimum (a value between 0.01 and 100). The best results in the adaptive filter were obtained from optimum @m value.