Acoustic Echo Cancellation System Research Articles

Nonlinear acoustic echo cancellation using low-complexity low-rank recursive least-squares algorithms

Adaptive exponential functional link networks (AEFLN) are a type of linear-in-the-parameter nonlinear filters, which have shown enhanced modeling capability for nonlinear systems. However, the convergence speed of traditional AEFLN is generally slow for long impulse responses, hence, AEFLN trained using recursive least-squares becomes an attractive choice to achieve faster convergence. Moreover, huge computational burden of RLS makes it unsuitable in practical applications like echo cancellation. While low complexity versions of RLS are widely available in literature, they still suffer from low convergence speed. To address this issue, we propose a nonlinear acoustic echo cancellation (NAEC) system using AEFLN-RLS, based on the nearest Kronecker product (NKP) decomposition and low-rank approximation technique, which not only reduces computational complexity but also achieves improved convergence speed (especially tracking). To further improve the echo cancellation performance in non-stationary conditions, a variable regularization approach based NKP-AEFLN-RLS system is also proposed. To also reduce the computational complexity further, dichotomous coordinate descent (DCD) updates are incorporated into the proposed NKP-AEFLN-RLS NAEC system and its variable regularization version. Experimental results show the effectiveness of the proposed algorithms, with the variable regularized version of the algorithm using DCD iterations showing the best compromise between convergence capability and lower computational complexity.

Acoustic echo cancellation system based on Laguerre method and neural network

Acoustic echo cancellation (AEC) is a fundamental requirement of signal processing to increase the quality of teleconferences. In this paper, a system that combines the Laguerre method with neural networks is proposed for AEC. In particular, the signal is processed using the Laguerre method to effectively handle nonlinear transmission line system. The results after applying the Laguerre method are then fed into a neural network for training and acoustic echo cancellation. The proposed system is tested on both linear and nonlinear transmission lines. Simulation results show that combining the Laguerre method with neural networks is highly effective for AEC in both linear and nonlinear transmission lines system. The AEC results obtained by the proposed method achieves a significant improvement in nonlinear transmission lines and it is the basis for building a practical echo cancellation system.

Acoustic echo cancellation system based on Laguerre method and neural network

Acoustic echo cancellation (AEC) is a fundamental requirement of signal processing to increase the quality of teleconferences. In this paper, a system that combines the Laguerre method with neural networks is proposed for AEC. In particular, the signal is processed using the Laguerre method to effectively handle nonlinear transmission line system. The results after applying the Laguerre method are then fed into a neural network for training and acoustic echo cancellation. The proposed system is tested on both linear and nonlinear transmission lines. Simulation results show that combining the Laguerre method with neural networks is highly effective for AEC in both linear and nonlinear transmission lines system. The AEC results obtained by the proposed method achieves a significant improvement in nonlinear transmission lines and it is the basis for building a practical echo cancellation system.

Joint Online Estimation of Early and Late Residual Echo PSD for Residual Echo Suppression

In hands-free telephony and other distant-talking applications, an acoustic echo cancellation system is typically required, where a short adaptive filter is often used in practice to achieve fast convergence at low computational cost. This may result in late residual echo (LRE) remaining due to under-modeling of the echo path and early residual echo (ERE) due to filter misalignment. Both residual echo components can be suppressed using a postfilter in the subband domain, which requires accurate estimates of the power spectral density (PSD) of the ERE and LRE components. State-of-the-art methods estimate the ERE and LRE PSDs independently of each other, where the ERE PSD is estimated by simply multiplying the loudspeaker PSD with a frequency-dependent scalar and the LRE PSD is estimated using a recursive estimator based on frequency-dependent reverberation scaling and decay parameters. In this paper, we propose to extend the ERE PSD estimator from a scalar to a moving average filter on the loudspeaker PSD. In addition, we propose a signal-based method to jointly estimate all model parameters for the ERE and LRE PSD estimators in online mode, and derive two gradient-descent-based algorithms to simultaneously update the model parameters by minimizing the mean squared log error. The proposed method is compared with state-of-the-art methods in terms of estimation accuracy of the model parameters as well as the residual echo PSDs. Simulation results using both artificially generated as well as measured impulse responses show that the proposed method outperforms state-of-the-art methods for all considered scenarios.

Dynamic Neural Network-based Solutions for Acoustic Echo Suppression

Adaptive filters are widely used as core algorithms in current acoustic echo cancellation (AEC) systems. Echo path estimation is carried out by using different adaptive strategies in the adaptive filter. When nonlinear echo occurs, the performance deteriorates and seriously affects the call quality of both terminals. Taking advantage of the excellent non-linear fitting ability of neural networks in this paper, dynamic neural networks with self-updating parameters work continuously during the inference stage. The algorithm was computed and evaluated using publicly available echo audio data, showing that the dynamic neural network performs approximately as well as the optimal algorithm in the linear echo environment, and outperforms existing algorithms in the non-linear echo environment.

Partial Update Simplified Fast Transversal Filter Algorithms for Acoustic Echo Cancellation

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 context of acoustic echo cancellation, have demonstrated that the proposed algorithms provide better convergence speed, good tracking capability and steady-state performances than the NLMS and SMFTF algorithms.

Nonlinear stereophonic acoustic echo cancellation using sub-filter based adaptive algorithm

The nonlinear echo path resulting from the unwarranted acoustic echo and nonlinear distortion will degrade the echo cancellation performance of a stereophonic acoustic echo cancellation system. Techniques proposed in the literature rely on the assumption of a linear echo path. In this paper, a nonlinear stereophonic acoustic echo cancellation framework is proposed to address the problem of nonlinear distortion in stereophonic acoustic cancellation systems with a better trade-off between convergence and steady-state performance. The proposed approach uses the functional link-based adaptive filtering to deal with the nonlinear distortion in combination with the sub-filter approach to enhance the convergence performance. In addition to that, the convergence analysis of the proposed algorithm is presented in this paper. Simulations carried out using echo return loss enhancement and magnitude squared coherence as performance metrics using speech and colored noise input demonstrate the proposed framework's ability to improve the echo cancellation performance in the presence of distortion.

Simple and Efficient Double-Talk-Detector for Acoustic Echo Cancellation

Acoustic Echo Cancellation (AEC) is a topic that has received a great interest in recent years. However, a significant challenge remains with the problem of double-talk especially when the adaptive filter has a fast convergence rate. In this case, the double-talk detector (DTD) must reply in early stage and halt updating of the adaptive filter in order to avoid filter coefficients divergence. Indeed, a complex and inappropriate DTD can seriously affect the convergence rate of the adaptive filter and global performances of the AEC system. In this paper, an implementation of a simple and efficient DTD based on a recursive estimation of the decision variable which is resulting from the level comparison between far-end and microphone signals is proposed. The presented algorithm is then compared with the normalized cross-correlation (NCC) method which is taken as a reference in this work. In the simulation tests, the recursive least squares (RLS) algorithm is used to update the adaptive filter coefficients. The speech signals used in the tests are taken from the TIMIT database.

A novel cascade structure for joint backward blind acoustic noise and echo cancellation systems

This paper addresses joint acoustic echo and punctual noise cancellations framework in hands-free communication systems. A novel cascade structure that combines the backward blind source separation structure (BBSS) as an acoustic noise canceller (ANC), and a conventional acoustic echo canceller (AEC), intended to solve the problem of acoustic echo, is presented. It is proposed to use two adaptive filtering algorithms to update the filter coefficients of each process (ANC and AEC stages). In the first ANC stage, the BBSS structure is combined with the two-channel simplified fast transversal (SFTF) filter algorithm. In the second AEC stage, a single-channel SFTF algorithm is used. Experimental results show both superiority and efficiency of the new structure in comparison with the AEC based SFTF system (without a joint process) in terms of echo return loss enhancement and mean square error gain criteria.

An integrated acoustic echo and noise cancellation system using cross-band adaptive filters and wavelet thresholding of multitaper spectrum

An efficient integrated system for acoustic echo and noise cancellation in hands free devices is proposed in this paper. Adaptive filters are usually employed in such systems for the acoustic echo cancellation. In order to reduce the computational complexity, sub band adaptive filters are commonly used since the order of filter required will be less but at the cost of distortion due to aliasing effect. In this work, cross-band adaptive filters are developed in wavelet domain/wavelet packet domain in tune with psychoacoustic model, to reduce the above distortion and thereby effectively estimate the echo and cancel it. The system uses wavelet thresholding technique for smoothening the log multitaper spectrum which eliminates the annoying musical noise in the processed output. To achieve efficient echo and noise cancellation, a novel switching mechanism to switch between wavelet filter bank and perceptual wavelet packet filter bank with most matching wavelet basis, from a dictionary of wavelet bases, depending on the type of noise present in each frame of the noisy speech is designed and developed in this work. The performance of the proposed integrated system is evaluated in terms of Acoustic Interference Cancellation (AIC) and Noise Attenuation (NA) under different Signal to Noise Ratio (SNR) conditions. The system exhibits very good performance even at very low SNRs close to −10 dB when compared with other such competitive systems.

Performance enhancement of Echo Cancellation Using a Combination of Partial Update ( PU) Methods and New Variable Length LMS (NVLLMS) Algorithm

In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%, and 16% using Periodic, Sequential, Stochastic, and M-max PU NVLLMS algorithms respectively, compared to that used by a full update method which is very important, especially in the application of mobile communication since the power consumption must be considered. In addition, the average ERLE and average Mean Square Error (MSE) for M-max PU NVLLMS are better than other proposed algorithms.

Hardware Implementation for the Echo Canceller System based Subband Technique using TMS320C6713 DSP Kit

The acoustic echo cancellation system is very important in the communication applications that are used these days; in view of this importance we have implemented this system practically by using DSP TMS320C6713 Starter Kit (DSK). The acoustic echo cancellation system was implemented based on 8 subbands techniques using Least Mean Square (LMS) algorithm and Normalized Least Mean Square (NLMS) algorithm. The system was evaluated by measuring the performance according to Echo Return Loss Enhancement (ERLE) factor and Mean Square Error (MSE) factor.

Efficient multichannel acoustic echo cancellation using constrained tap selection schemes in the subband domain

Acoustic echo cancellation (AEC) is a key speech enhancement technology in speech communication and voice-enabled devices. AEC systems employ adaptive filters to estimate the acoustic echo paths between the loudspeakers and the microphone(s). In applications involving surround sound, the computational complexity of an AEC system may become demanding due to the multiple loudspeaker channels and the necessity of using long filters in reverberant environments. In order to reduce the computational complexity, the approach of partially updating the AEC filters is considered in this paper. In particular, we investigate tap selection schemes which exploit the sparsity present in the loudspeaker channels for partially updating subband AEC filters. The potential for exploiting signal sparsity across three dimensions, namely time, frequency, and channels, is analyzed. A thorough analysis of different state-of-the-art tap selection schemes is performed and insights about their limitations are gained. A novel tap selection scheme is proposed which overcomes these limitations by exploiting signal sparsity while not ignoring any filters for update in the different subbands and channels. Extensive simulation results using both artificial as well as real-world multichannel signals show that the proposed tap selection scheme outperforms state-of-the-art tap selection schemes in terms of echo cancellation performance. In addition, it yields almost identical echo cancellation performance as compared to updating all filter taps at a significantly reduced computational cost.

Diffusion leaky LMS algorithm: Analysis and implementation

The diffusion least-mean-square (dLMS) algorithms have attracted much attention owing to its robustness for distributed estimation problems. However, the performance of such algorithms may change when they are implemented for acoustic echo cancellation (AEC) systems. To overcome this problem, a leaky dLMS algorithm is proposed in this work, which is characterized by its numerical stability and small steady-state error for noisy speech signals. Then, we perform some stability and convergence analyses of the proposed algorithm for Gaussian inputs and verify the theory results by simulations. As an added contribution in this paper, we further develop a new variable leakage factor (VLF) strategy for the leaky dLMS algorithm to overcome the parameter selection of adaptation. Finally, implementations of the proposed algorithms in the context of system identification and stereophonic AEC (SAEC) network are performed. Simulation results illustrate that the leaky diffusion algorithms achieve improved performance as compared with the existing algorithms.

A Computationally Efficient Delayless Frequency-Domain Adaptive Filter Algorithm

In acoustic echo cancellation (AEC) systems, the partitioned block frequency-domain adaptive filter (PBFDAF) algorithm is commonly adopted to improve the computational efficiency and convergence rate. However, the PBFDAF algorithm introduces an inherent delay. Delayless PBFDAF algorithms have been proposed to tackle this issue. However, the complexity of the existing delayless PBFDAF algorithms is high. Some have high average complexity, but others have high peak complexity. A computationally efficient delayless PBFDAF algorithm is proposed in this letter to reduce both the average and peak complexity. Moreover, a delay compensation method is presented to compensate the error path delay and thus speed up the convergence rate. Simulation results demonstrate that the convergence and tracking performance of the new algorithm with delay compensation is comparable with that of the PBFDAF algorithm.

An efficient post-processing filter for multi-channel acoustic echo cancellation system

The acoustic echo usually cannot be totally removed by the adaptive filter due to the insufficient filter taps, nonlinearity of the echo path transfer function, and the background noise. A post-processing filter, which aims to suppress the residual echo, thus plays an important role in the acoustic echo cancellation system. In this paper, an efficient post filter is proposed based on the coherence between the error signal and the echo estimation of the adaptive filter. The proposed method can be seamlessly combined with the multi-delay frequency-domain adaptive filter. Furthermore, it can be easily extended to multi-channel processing. The instrumental evaluation and listening test both demonstrate the superiority of the proposed method.

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.

Double‐talk detector based on audio watermarking.

The double‐talk detection (DTD) algorithm is a vital part of an acoustic echo cancellation system commonly used in the telecommunication applications. The role of the DTD algorithm is to stop the adaptation process of the adaptive filter used for the estimation of echo path response in the presence of double‐talk condition. The typically used DTD solutions are often a compromise between high efficiency and affordable computational cost. The algorithm introduced by the authors is based on a novel principle related to the so‐called semi‐fragile watermarking, making it possible to achieve satisfactory performance with limited processing burden. A hidden signature is embedded into the arriving far‐end speaker signal just before it is replayed to the near‐end speaker. Consequently, the presence of that signature is detected in the signal recorded by the near‐end speaker microphone. The appropriate choice of the employed watermarking technique causes the signature to become undetectable during the near‐end speaker’s talkspurts, precisely identifying the double‐talk periods. The use of echo‐hiding watermarking method allows fulfilling the algorithm requirements. The results of performed experiments prove the good performance of the proposed DTD solution. [Research funded by the Polish Ministry of Science and Higher Education within Grant No. PBZ‐MNiSW‐02‐II‐2007.]

Adaptation Control Parameters of Multipath Model of an Adaptive Acoustic Echo Canceller

An overview of acoustic echo cancellation systems is presented. First the basics of echo’s, type of echo’s , why echo canceller are required and what are the difference between echo canceller are explained then the application where they can be used is discussed.. This paper presents applications of acoustic echo control and shows most recent solutions. For some selected applications (LEM) results achieved are presented.

Delay Coefficients Based Variable Regularization Subband Affine Projection Algorithms in Acoustic Echo Cancellation Applications

To attain good performance in an acoustic echo cancellation system, it is important to have a variable step size (VSS) algorithm as part of an adaptive filter. In this paper, we are concerned with the development of a VSS algorithm for a recently proposed subband affine projection (SAP) adaptive filter. Two popular VSS algorithms in the literature are the methods of delayed coefficients (DC) and variable regularization (VR). However, the merits and demerits of them are mutually exclusive. We propose a VSS algorithm* that is a hybrid of both methods and combines their advantages. An extensive study of the new algorithm in different scenarios like the presence double-talk (DT) during the transient phase of the adaptive filter, DT during steady state, and varying DT power is conducted and reasoning is given to support the observed behavior. The importance of the method of VR as part of a VSS algorithm is emphasized.