Acoustic Echo Cancellation Problem Research Articles

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.

Collaborative block-delay Volterra filters for nonlinear acoustic echo cancellation

A new nonlinear adaptive filtering strategy, collaborative block-delay Volterra filters (CBDVF), is proposed for nonlinear acoustic echo cancellation (NAEC). This paper focuses on the theoretical reasonability and practical feasibility of the proposed algorithm. Specifically, a practice-friendly adaptive filtering architecture is designed to ensure that the linear filter is considered a priority to play the most important role in estimating the echo path while the nonlinear filters will further perfect the modeling nonlinearities. A CBDVF is obtained by a combination of linear filter and different nonlinear filtering blocks of different time-delay length, where the nonlinear time-delay blocks are activated or deactivated automatically and adaptively to fulfil their tasks via some adaptive shrinkage factors. Theoretical performance analysis is also conducted to characterize the stability condition of the proposed algorithm. Simulations with an acoustic echo cancellation problem demonstrate the effectiveness of the proposed CBDVF. Moreover, the evaluation for CBDVF is completed for a real NAEC scenario.

Estimating Parameters of Nonlinear Systems Using the Elitist Particle Filter Based on Evolutionary Strategies

In this paper, we present the elitist particle filter based on evolutionary strategies EPFES as an efficient approach to estimate the statistics of a latent state vector capturing the relevant information of a nonlinear system. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection scheme which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this paper, we propose two advancements of the previously published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter: First, we consider the univariate nonstationary growth model with time-variant latent state variable to evaluate the tracking capabilities of the EPFES for instantaneously calculated particle weights. This is followed by addressing the problem of single-channel nonlinear acoustic echo cancellation as a challenging benchmark task for identifying an unknown system of large search space: the nonlinear acoustic echo path is modeled by a cascade of a parameterized preprocessor to model the loudspeaker signal distortions and a linear FIR filter to model the sound wave propagation and the microphone. By using long-term information, we highlight the efficacy of the well-generalizing EPFES in estimating the preprocessor parameters for a simulated scenario and a real smartphone recording. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.

Convex Combined Adaptive Filtering Algorithm for Acoustic Echo Cancellation in Hostile Environments

Research into combined adaptive filters has been attracting considerable attention; the performance of which is generally superior to that of the individual component filters. Formulating an adaptive filtering algorithm to handle a range of cases can be very difficult, particularly in complicated scenarios. This paper applies a robust convex combination method to the problem of acoustic echo cancellation. According to the minimum mean square error criterion, we first derive the optimal combination factor for a combination adaptive filter with two component filters. Next, we provide an approximation method to the inaccessible optimal combination factor. Finally, we extend this approximated factor to a robust one so that it is applicable even in situations with strong a ambient impulsive noise (IN) and abrupt changes in the echo channel. To do this, we non-linearly transfer the approximated combination factor so that this combination factor exhibits less transient time during the adaptation process of the combination factor and a fewer fluctuations in its steady state. Moreover, we detect the presence of the strong ambient IN and force the combined system output mainly contributed by one of the two component adaptive filters which is less sensitive to the the strong IN. We adopted computer simulations combining two affine projection sign algorithms with different step sizes as an example. Simulation results show that the proposed method is superior to comparable algorithms in terms of the normalized mean squared deviation and echo return loss enhancement.

Application of Digital Signal Processing In Echo Cancellation: A Survey

The advanced communications world is worried talking more naturally by using hands free this help the human being to talk more confidently without holding any of the devices such as microphones or telephones. Acoustic echo cancellation and noise cancellers are quite interesting nowadays because they are required in many applications such as speakerphones and audio/video conferencing. This paper describes an alternative method of estimating signals corrupted by additive noise or interference. Acoustic echo cancellation problem was discussed out of different noise cancellation techniques by concerning different parameters with their comparative results .The results shown are using some specific algorithms.

Single-channel acoustic echo cancellation in noise based on gradient-based adaptive filtering

In this paper, a two-stage scheme is proposed to deal with the difficult problem of acoustic echo cancellation (AEC) in single-channel scenario in the presence of noise. In order to overcome the major challenge of getting a separate reference signal in adaptive filter-based AEC problem, the delayed version of the echo and noise suppressed signal is proposed to use as reference. A modified objective function is thereby derived for a gradient-based adaptive filter algorithm, and proof of its convergence to the optimum Wiener-Hopf solution is established. The output of the AEC block is fed to an acoustic noise cancellation (ANC) block where a spectral subtraction-based algorithm with an adaptive spectral floor estimation is employed. In order to obtain fast but smooth convergence with maximum possible echo and noise suppression, a set of updating constraints is proposed based on various speech characteristics (e.g., energy and correlation) of reference and current frames considering whether they are voiced, unvoiced, or pause. Extensive experimentation is carried out on several echo and noise corrupted natural utterances taken from the TIMIT database, and it is found that the proposed scheme can significantly reduce the effect of both echo and noise in terms of objective and subjective quality measures.

An Efficient Adaptive Filtering Scheme Based on Combining Multiple Metrics

SUMMARY We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.

Widely linear general Kalman filter for stereophonic acoustic echo cancellation

The stereophonic acoustic echo cancellation (SAEC) problem is usually modelled as a two-input/two-output system with real random variables. Recently, the SAEC scheme was recast as a single-input/single-output system with complex random variables, thanks to the widely linear (WL) model. In this paper, we motivate the use of a more general form of the Kalman filter with the WL model for SAEC. Simulation results indicate that this algorithm outperforms the recursive least-squares (RLS) algorithm, which is usually considered as the benchmark for SAEC.

Learning Echo Paths During Continuous Double-Talk Using Semi-Blind Source Separation

Echo cancelers typically employ control mechanisms to prevent adaptive filter updates during double-talk events. By contrast, this paper exploits the information contained in time-varying second order statistics of nonstationary signals to update adaptive filters and learn echo path responses during double-talk. First, a framework is presented for describing mixing and blind separation of independent groups of signals. Then several echo cancellation problems are cast in this framework, including the problem of simultaneous acoustic and line echo cancellation as encountered in speaker phones. A maximum-likelihood approach is taken to estimate both the unknown signal statistics as well as echo canceling filters. When applied to speech signals, the techniques developed in this paper typically achieved between 30 and 40 dB of echo return loss enhancement (ERLE) during continuous double-talking.

Recursive Bayesian Control of Multichannel Acoustic Echo Cancellation

We present a novel recursive Bayesian method in the DFT-domain to address the multichannel acoustic echo cancellation problem. We model the echo paths between the loudspeakers and the near-end microphone as a multichannel random variable with a first-order Markov property. The incorporation of the near-end observation noise, in conjunction with the multichannel Markov model, leads to a multichannel state-space model. We derive a recursive Bayesian solution to the multichannel state-space model, which turns out to be well suited for input signals that are not only auto-correlated but also cross-correlated. We show that the resulting multichannel state-space frequency-domain adaptive filter (MCSSFDAF) can be efficiently implemented due to the submatrix-diagonality of the state-error covariance. The filter offers optimal tracking and robust adaptation in the presence of near-end noise and echo path variability.

Stereo Acoustic Echo Cancellation Employing Frequency-Domain Preprocessing and Adaptive Filter

This paper proposes a windowing frequency domain adaptive filter and an upsampling block transform preprocessing to solve the stereo acoustic echo cancellation problem. The proposed adaptive filter uses windowing functions with smooth cutoff property to reduce the spectral leakage during filter updating, so the utilization of the independent noise introduced by preprocessing in stereo acoustic echo cancellation can be increased. The proposed preprocessing is operated in short blocks with low processing delay, and it uses frequency-domain upsampling to meet the minimal block length requirement given by the band limit of simultaneous masking. Therefore, the simultaneous masking can be well utilized to improve the audio quality. The acoustic echo cancellation simulations and the audio quality evaluation show that, the proposed windowing frequency domain adaptive filter performs better than the conventional frequency domain adaptive filter in both mono and stereo cases, and the upsampling block transform preprocessing provides better audio quality and stereo acoustic echo cancellation performance than the half-wave preprocessing at the same noise level.

Time-Reversal Approach to the Stereophonic Acoustic Echo Cancellation Problem

Stereophonic acoustic echo cancellation (SAEC) plays an important role in delivering realistic teleconferencing experience. The fundamental problem of SAEC system is that stereophonic channels are linearly related and this results in slow convergence of the adaptive filters. In this paper, we present a novel algorithm by employing a selective time-reversal block to solve the SAEC problem which results in a significant increase in the convergence performance of adaptive filters such that the stereophonic image as well as quality are preserved. The proposed algorithm employs time-reversal operation on selective blocks of input data samples for one of the two channels to decorrelate stereophonic channels in the SAEC system. To achieve good stereophonic perception, time-reversal operation is only applied to the selective blocks whose magnitudes fall below a pre-determined threshold. Theoretical and numerical simulation results are also studied and investigated to show that the proposed algorithm achieves faster convergence in terms of normalized misalignment and better stereophonic perception with less audio distortion compared to the well-known nonlinear transformation algorithm for the SAEC system.

Analysis of the Stereophonic LMS/Newton Algorithm and Impact of Signal Nonlinearity on Its Convergence Behavior

The strong cross-correlation that exists between the two input audio channels makes the problem of stereophonic acoustic echo cancellation (AEC) complex and challenging to solve. Recently, two new implementations of the LMS/Newton algorithm that uses a linear decorrelation technique were proposed. This method helps to mitigate the effect of the ill-conditioned problem on the convergence rate of the LMS/Newton adaptive algorithm. The complexity of these algorithms is significantly lower than the recursive least-squares (RLS) algorithm, which is known to provide excellent echo cancellation. Furthermore, unlike the various versions of the RLS algorithm, the LMS/Newton algorithm is more robust to numerical errors. It has also been suggested that applying nonlinearities to signals at the two audio channels will help to alleviate the misalignment problem of stereophonic AEC systems. Simulation studies reveal that application of certain classes of nonlinearities to the two-channel LMS/Newton algorithms helps to further reduce the misalignment but it also leads to an unexpected and significant reduction in the rate of convergence of the mean-square error. The contributions of this paper are twofold. First, we provide an analysis of the two-channel LMS/Newton algorithm that was proposed in our earlier work. Second, we provide a theoretical understanding for the appearance of the slow modes of convergence in the presence of nonlinearities and show that they can be resolved through a preprocessing step.

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 improved new error estimation algorithm for optimal filter lengths for stereophonic acoustic echo cancellation

In this paper, we propose an new error estimate algorithm (NEEA) for stereophonic acoustic echo cancellation (SAEC) that is based on the error estimation algorithm (EEA) in [Nguyen-Ky T, Leis J, Xiang W. An improved error estimate algorithm for stereophonic acoustic echo cancellation system. In: International conference on signal processing and communication systems, ICSPCS’2007, Australia; December 2007]. In the EEA and NEEA, with the minimum error signal fixed, we compute the filter lengths so that the error signal may approximate the minimum error signal. When the echo paths change, the adaptive filter automatically adjusts the filter lengths to the optimum values. We also investigate the difference between the adaptive filter lengths. In contrast with the conclusions in [Khong AWH, Naylor PA. Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms. IEEE Trans Audio, Speech, Lang Process 2006;14(3):785–96, Gansler T, Benesty J. Stereophonic acoustic echo cancellation and two channel adaptive filtering: an overview. Int J Adapt Control Signal Process 2000;4:565–86, Benesty J, Gansler T. A multichannel acoustic echo canceler double-talk detector based on a normalized cross-correlation matrix. Acoust Echo Noise Control 2002;13(2):95–101, Gansler T, Benesty J. A frequency-domain double-talk detector based on a normalized cross-correlation vector. Signal Process 2001;81:1783–7, Eneroth P, Gay SL, Gansler T, Benesty J. A real-time implementation of a stereophonic acoustic echo canceler. IEEE Trans. Speech Audio Process 2001;9(5):513–23, Gansler T, Benesty J. New insights into the stereophonic acoustic echo cancellation problem and an adaptive nonlinearity solution. IEEE Trans. Speech Audio Process 2002; 10(5):257–67, Benesty J, Gansler T, Morgan DR, Sondhi MM, Gay SL. Advances in network and acoustic echo cancellation. Berlin: Springer-Verlag; 2001], our simulation results have shown that the filter lengths can be different. Our simulation results also confirm that the NEEA is better than EEA and SM-NLMS algorithm in terms of echo return loss enhancement.

Acoustic Echo Cancellation Using a Pseudocoherence Function in the Presence of Memoryless Nonlinearity

Acoustic echo cancellation (AEC) is critical for telecommunication applications involving two or more locations such as teleconferencing. It is also challenging because of loudspeaker's nonlinearity, real-time implementation requirement, and multipath effects of indoor environments. This paper addresses the nonlinear AEC problem. We use a Hammerstein model to describe the memoryless nonlinearity of loudspeaker concatenated with a linear room impulse response. We propose a method using a pseudo magnitude squared coherence (MSC) function to identify the nonlinearity in the Hammerstein system and develop an on-line AEC algorithm. Our method identifies nonlinearity without knowing the linear block in the Hammerstein system, which guarantees the stability of the algorithm and leads to a faster convergence rate. Moreover, several alternative criteria based on the MSC function are also proposed for nonlinearity identification. Effectiveness of the proposed algorithms is demonstrated through computer simulations.

Efficient Fast Stereo Acoustic Echo Cancellation Based on Pairwise Optimal Weight Realization Technique

In stereophonic acoustic echo cancellation (SAEC) problem, fast and accurate tracking of echo path is strongly required for stable echo cancellation. In this paper, we propose a class of efficient fast SAEC schemes with linear computational complexity (with respect to filter length). The proposed schemes are based on pairwise optimal weight realization (POWER) technique, thus realizing a best strategy (in the sense of pairwise and worst-case optimization) to use multiple-state information obtained by preprocessing. Numerical examples demonstrate that the proposed schemes significantly improve the convergence behavior compared with conventional methods in terms of system mismatch as well as echo return loss enhancement (ERLE).

New insights into the stereophonic acoustic echo cancellation problem and an adaptive nonlinearity solution

We expand the knowledge regarding the problems of two-channel (or stereophonic) echo cancellation. The major difference between two-channel and the single-channel echo cancellation is the problem of nonunique solutions in the two-channel case. In previous work, this nonuniqueness problem has been linked to the coherence between the two incoming audio channels. One proven solution to this problem is to distort the signals with a nonlinear device. In this work, we present new theory that gives insight to the existing links between: (i) coherence and level of distortion, and (ii) coherence and achievable misalignment of the stereophonic echo canceler. Furthermore, we present an adaptive nonlinear device that incorporates this new knowledge in such a way that a pre-specified maximum misalignment is maintained while improving the perceived quality by minimizing the introduced distortion. Moreover, all the ideas presented can be generalized to the multichannel (>2) case.

A new method for low-rank transform domain adaptive filtering

This paper introduces a least squares, matrix-based framework for adaptive filtering that includes normalized least mean squares (NLMS), affine projection (AP) and recursive least squares (RLS) as special cases. We then introduce a method for extracting a low-rank underdetermined solution from an overdetermined or a high-rank underdetermined least squares problem using a part of a unitary transformation. We show how to create optimal, low-rank transformations within this framework. For obtaining computationally competitive versions of our approach, we use the discrete Fourier transform (DFT). We convert the complex-valued DFT-based solution into a real solution. The most significant bottleneck in the optimal version of the algorithm lies in having to calculate the full-length transform domain error vector. We overcome this difficulty by using a statistical approach involving the transform of the signal rather than that of the error to estimate the best low-rank transform at each iteration. We also employ an innovative mixed domain approach, in which we jointly solve time and frequency domain equations. This allows us to achieve very good performance using a transform order that is lower than the length of the filter. Thus, we are able to achieve very fast convergence at low complexity. Using the acoustic echo cancellation problem, we show that our algorithm performs better than NLMS and AP and competes well with FTF-RLS for low SNR conditions. The algorithm lies in between affine projection and FTF-RLS, both in terms of its complexity and its performance.

Analysis of Different Low Complexity Nonlinear Filters for Acoustic Echo Cancellation

Linear filters are often employed in most signal processing applications. As a matter of fact, they are well understood within a uniform theory of discrete linear systems. However, many physical systems exhibit some nonlinear behaviour, and in certain situations linear filters perform poorly. One case is the problem of acoustic echo cancellation, where the digital filter employed has to identify as close as possible the acoustic echo path that is found to be highly nonlinear. In this situation a better system identification can be achieved by a nonlinear filter. The problem is to find a nonlinear filter structure able to realize a good approximation of the echo path without any significant increase of the computational load. Conventional Volterra filters are well suited for modelling that system but they generally need too many computational resources for a real time implementation. In this paper we consider some low complexity nonlinear filters in order to find out a filter structure able to achieve performances close to those of the Volterra filter, but with a reduced increase of the computational load in comparison to the linear filters commonly employed in commercial acoustic echo cancellers.