Echo Path Research Articles

Nonlinear Acoustic Echo Canceller to Combat Sigmoid-Type Nonlinearities Under Noisy Environment

This paper presents a nonlinear-acoustic-echo-cancellation (NAEC) technique to tackle sigmoid-type nonlinearities under noisy environment. The nonlinear echo in acoustic systems is inevitable due to the inherent nonlinear characteristics of amplifiers and/or loudspeakers, which deteriorates the quality of speech as well as audio signal reception. Here, the sigmoid-type nonlinearity is modelled by incorporating two control parameters, which determine the shaping- and clipping-parameter values of the saturation curve at a particular room temperature. These control parameters are adjusted by utilizing the variable-step-size (VSS) least-mean-square (LMS) algorithm to enhance the convergence rate and tracking capability of presented NAEC. Furthermore, the impulse response of a room (indoor channel) in the acoustic echo path is modelled as a tap-delay-line finite-impulse-response filter, whose tap-coefficients are estimated by utilizing a modified recursive-least-squares (RLS) algorithm (involving the noise statistics) at the different values of signal-to-noise-ratio (SNR), when correlated as well as uncorrelated input signals are processed. Simulation results demonstrate the efficiency and efficacy of above mentioned adaptive NAEC technique using the VSS-LMS and modified RLS algorithms in terms of the high convergence rate as well as high value of echo-return-loss-enhancement (ERLE) factor. Both the elevating value of shaping-parameter (i.e., increasing nonlinearity level) and the alleviating value of SNR adversely affect the performance of all NAECs. However, the VSS-LMS and modified RLS algorithm based presented adaptive NAEC outperforms the traditional VSS-LMS and normalized-least-mean-square (NLMS) algorithm based NAEC under similar conditions.

Nonlinear acoustic echo cancellation with kernelized adaptive filters

A well-known problem in speech communication is the occurrence of acoustic echo in hands-free telephony. There are several classical adaptive filters that have been used as a stand alone approach for linear acoustic echo cancellation(AEC). A common assumption in most AEC techniques is that the echo path is linear. However, in real scenarios, owing to distortions introduced by other acoustic artefacts in handheld devices, the echo path is nonlinear. Therefore, there is a need to employ a nonlinear echo canceller. In this paper, a kernel expansion on a family of adaptive filtering algorithms based on least mean square filter is proposed. Kernel methods help model the nonlinear echo path and attain global minimum easily by finding the optimal set of filter weights. The simulations are carried out for speech signal with and without noise under different signal-to-noise ratio values. The results show that the proposed method achieves a significant improvement in a nonlinear acoustic echo cancellation environment in terms of echo return loss enhancement.

Double-talk robust acoustic echo canceller based on CNN filter

Conventional acoustic echo cancellation works by using an adaptive algorithm to identify the impulse response of the echo path. In this paper, we use the CNN neural network filter to remove the echo signal from the microphone input signal, so that only the speech signal is transmitted to the far-end. Using the neural network filter, weights are well converged by the general speech signal. Especially it shows the ability to perform stable operation without divergence even in the double-talk state, in which both parties speak simultaneously. As a result of simulation, this system showed superior performance and stable operation compared to the echo canceller of the adaptive filter structure.

Double-talk robust acoustic echo canceller based on CNN filter

Conventional acoustic echo cancellation works by using an adaptive algorithm to identify the impulse response of the echo path. In this paper, we use the CNN neural network filter to remove the echo signal from the microphone input signal, so that only the speech signal is transmitted to the far-end. Using the neural network filter, weights are well converged by the general speech signal. Especially it shows the ability to perform stable operation without divergence even in the double-talk state, in which both parties speak simultaneously. As a result of simulation, this system showed superior performance and stable operation compared to the echo canceller of the adaptive filter structure.

Stereo Acoustic Echo Cancellation Based on Maximum Likelihood Estimation With Inter-Channel-Correlated Echo Compensation

This paper presents batch and online algorithms of a stereo acoustic echo cancellation (SAEC) method. In SAEC, the non-uniqueness problem causes performance degradation, especially for highly coherent far-end signals. In our method, this problem can be avoided without an additional decorrelation preprocessor or multi-microphones by overestimating far-end echoes and compensating for the overestimated inter-channel-correlated echo to obtain a desired echo-canceled signal. In addition, our method is based on the maximum likelihood estimation (MLE) criterion of the echo-canceled signal under the assumption that the signal in the time-frequency domain follows a zero-mean complex Gaussian distribution with a time-varying variance. Furthermore, a variable forgetting factor based on the cross spectral density (CSD) between the echo-canceled signal and a far-end echo is presented in the online algorithm to improve the convergence of adaptive filters with a high cancellation performance when converged. Experimental results under various conditions demonstrate that the proposed method can successfully perform SAEC even in the presence of inter-channel correlation, double-talk, and abrupt echo path changes.

Artifacts in Grayscale Ultrasound

Image artifacts are frequently encountered in clinical ultrasound. While some of them may be a source of misinterpretation, others may provide useful information related to the composition of underlying tissue. Majority of these artifacts is caused by violation of basic physical assumptions to assign the location and intensity of the received echo. Ultrasound artifacts arise secondary to errors related to multiple echo paths, ultrasound beam characteristics, velocity errors, or attenuation errors. Understanding the fundamental ultrasound physics and recognizing common ultrasound artifacts is essential for proper image interpretation and optimal patient care. Keywords: Ultrasound; Artifact 중심 단어: 초음파; 허상

Robust adaptive filter with lncosh cost

In this paper, a least lncosh (Llncosh) algorithm is derived by utilizing the lncosh cost function. The lncosh cost is characterized by the natural logarithm of hyperbolic cosine function, which behaves like a hybrid of the mean square error (MSE) and mean absolute error (MAE) criteria depending on adjusting a positive parameter λ. Hence, the Llncosh algorithm performs like the least mean square (LMS) algorithm for small errors and behaves as the sign-error LMS (SLMS) algorithm for large errors. It provides comparable performance to the LMS algorithm in Gaussian noise. When compared with several existing robust approaches, the superior steady-state performance and stronger robustness can be attained in impulsive noise. The mean behavior, mean-square behavior and steady-state performance analyses of the proposed algorithm are also provided. In addition, aiming to acquire a compromise between fast initial convergence rate and satisfactory steady-state performance, we introduce a variable-λ Llncosh (VLlncosh) scheme. Lastly, in order to resist the sparsity of the acoustic echo path, an improved proportionate least lncosh (PLlncosh) algorithm is presented. The good performance against impulsive noise and theoretical results of the proposed algorithm are validated by simulations.

An efficient Kalman filter for the identification of low-rank systems

System identification problems are very difficult in the scenario of long length impulse responses, raising challenges in terms of convergence, complexity, and accuracy of the solution. However, we can take advantage of the characteristics of the impulse response, in order to improve the overall performance. In this context, a recently introduced approach exploits a Kronecker product decomposition of the impulse response in tandem with low-rank approximations. Also, a recursive least-squares (RLS) algorithm was developed based on this idea, showing appealing results for the identification of low-rank systems, like typical echo paths. In this short communication, we propose a Kalman filter tailored for the identification of such low-rank systems. Simulations performed in the context of echo cancellation indicate that the proposed algorithm outperforms the regular Kalman filter, but also its RLS-based counterpart.

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.

The removal method of the influence of heartbeat in the ultrasonic velocity-change method

To non-invasively identify an unstable plaque using the ultrasonic velocity-change (UVC) method, we examined a method for removing influences of motion such as heartbeat which causes a fluctuation of echo path when applying the UVC method to the living body. In conclusion, by using an M-mode waveform to obtain the UVC data, fat portions in a carotid artery phantom under the pseudo environment of a heartbeat could be identified in a very high success rate and in a short time.

Recursive Least-Squares Algorithms for the Identification of Low-Rank Systems

The recursive least-squares (RLS) adaptive filter is an appealing choice in many system identification problems. The main reason behind its popularity is its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the identification of long length impulse responses, like in echo cancellation. Computationally efficient versions of the RLS algorithm, like those based on the dichotomous coordinate descent (DCD) iterations or QR decomposition techniques, reduce the complexity, but still have to face the challenges related to long length adaptive filters (e.g., convergence/tracking capabilities). In this paper, we focus on a different approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. In other words, a high-dimension system identification problem is reformulated in terms of low-dimension problems, which are combined together. This approach was recently addressed in terms of the Wiener filter, showing appealing features for the identification of low-rank systems, like real-world echo paths. In this paper, besides the development of the RLS algorithm based on this approach, we also propose a variable regularized version of this algorithm (using the DCD method to reduce the complexity), with improved robustness to double-talk. Simulations are performed in the context of echo cancellation and the results indicate the good performance of these algorithms.

Linear System Identification Based on a Kronecker Product Decomposition

Linear system identification is a key problem in many important applications, among which echo cancelation is a very challenging one. Due to the long length impulse responses (i.e., echo paths) to be identified, there is always room (and needs) to improve the performance of the echo cancelers, especially in terms of complexity, convergence rate, robustness, and accuracy. In this paper, we propose a new way to address the system identification problem (from the echo cancelation perspective), by exploiting an optimal approximation of the impulse response based on the nearest Kronecker product decomposition. Also, we make a first step toward this direction, by developing an iterative Wiener filter based on this approach. As compared to the conventional Wiener filter, the proposed solution is much more attractive since its gain is twofold. First, the matrices to be inverted (or, preferably, linear systems to be solved) are smaller as compared to the conventional approach. Second, as a consequence, the iterative Wiener filter leads to a good estimate of the impulse response, even when a small amount of data is available for the estimation of the statistics. Simulation results support the theoretical findings and indicate the good results of the proposed approach, for the identification of different network and acoustic impulse responses.

A New Decision-Theory-Based Framework for Echo Canceler Control

A control logic has a central role in many echo cancellation systems for optimizing the performance of adaptive filters while estimating the echo path. For reliable control, accurate double-talk (DT) and channel change (CC) detectors are usually incorporated to the echo canceler. This work expands the usual detection strategy to define a classification problem characterizing four possible states of the echo canceler operation. The new formulation allow the use of decision theory to continuously control the transitions among the different modes of operation. The classification rule reduces to a low cost statistics for which it is possible to determine the probability of error under all hypotheses, allowing the classification performance to be accessed analytically. Monte Carlo simulations using synthetic and real data illustrate the reliability of the proposed method.

Block‐sparsity‐aware LMS algorithm for network echo cancellation

Network echo path impulse response is single-block-sparse in nature. In order to obtain a single-block-sparse estimate of the unknown echo path, a new least mean squares (LMS) algorithm is proposed by introducing the penalty of single block sparsity, which is the difference between the mixed l 2 , 1 norm and l 2 norm of the uniformly partitioned filter tap-weight vector, into the original mean-square-error cost function. This is motivated by the fact that the difference between the mixed l 2 , 1 norm and l 2 norm of a vector is minimised only when there is at most one non-zero block in the vector. Numerical simulation results show that the proposed algorithm can effectively estimate and track the unknown echo path, outperforming existing block-sparsity-induced LMS algorithms.

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.

Nonlinear Acoustic Echo Cancellation Based on Multichannel Adaptive Filters: A Novel Approach

This paper presented a new class of non-linear adaptive filters, whose structure is based on Hammerstein model. Such filters designed from the functional link adaptive filter (FLAF) model, described by a non-linear input expansion which develops the illustration of the input signal through a projection in a higher dimensional space and a subsequent adaptive filtering. In order to give toughness against different degrees of non-linearity, a collaborative FLAF is proposed based on the adaptive combination of filters. Such architecture provided the best performance regardless of the nonlinearity degree in the echo path. Particularly this architecture can accomplish the linear and non-linear filters. Such as Normalized least mean square algorithm using FDAF, Affine projection and Lattice filters are used. When comparing their Echo return loss enhancement performance, Affine projection algorithm gives 20 dB more than others in this work.

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.

Statistical Convergence Analysis for Optimal Control of DFT-Domain Adaptive Echo Canceler

The frequency-domain adaptive filter (FDAF) is widely used in echo cancellation systems due to its low complexity and fast convergence rate. However, the FDAF algorithm with a fixed step size exhibits a tradeoff among the convergence rate, steady-state misalignment, tracking ability, and robustness to near-end speech interferences. Several variable step-size FDAF algorithms were presented to address this problem. However, the state-of-the-art variable step-size FDAF algorithms did not handle this problem comprehensively. This paper presents a new robust variable step-size control approach to the FDAF algorithm. Based on a statistical analysis of the FDAF algorithm, an optimal step size for each frequency bin is derived by minimizing the mean-square deviation (MSD) between the true weight vector and estimated weight vector at each frame. Calculation of the step size requires the system distance and the observation noise power spectral density (PSD). The system distance is estimated using the deterministic recursive equations of MSD and the noise PSD is computed using the magnitude squared coherence function between the far-end signal and error signal. Moreover, a close link between the proposed FDAF and the frequency-domain Kalman filter is revealed. Specifically, the work presented here can be understood as a means to adaptively monitor and control the underlying acoustic state space of the Kalman filter, including means for fast readaptation of the adaptive filter after abrupt echo path changes. Simulation results demonstrate that the proposed algorithm can achieve fast convergence and low steady-state misalignment. Furthermore, the algorithm is robust to the double-talk interferences, but it does not require an explicit double-talk detector.

Significance-aware filtering for nonlinear acoustic echo cancellation

This article summarizes and extends the recently proposed concept of Significance-Aware (SA) filtering for nonlinear acoustic echo cancellation. The core idea of SA filtering is to decompose the estimation of the nonlinear echo path into beneficially interacting subsystems, each of which can be adapted with high computational efficiency. The previously proposed SA Hammerstein Group Models (SA-HGMs) decompose the nonlinear acoustic echo path into a direct-path part, modeled by a Hammerstein Group Model (HGM) and a complementary part, modeled by a very efficient Hammerstein model. In this article, we furthermore propose a novel Equalization-based SA (ESA) structure, where the echo path is equalized by a linear filter to allow for an estimation of the loudspeaker nonlinearities by very small and efficient models. Additionally, we provide a novel in-depth analysis of the computational complexity of the previously proposed SA and the novel ESA filters and compare both SA filtering approaches to each other, to adaptive HGMs, and to linear filters, where fast partitioned-block frequency-domain realizations of the competing filter structures are considered. Finally, the echo reduction performance of the proposed SA filtering approaches is verified using real recordings from a commercially available smartphone. Beyond the scope of previous publications on SA-HGMs, the ability of the SA filters to generalize for double-talk situations is explicitly considered as well. The low complexity as well as the good echo reduction performance of both SA filters illustrate the potential of SA filtering in practice.

Tap-length optimization of adaptive filters used in stereophonic acoustic echo cancellation

An adaptive filter with a large number of weights or taps is necessary for stereophonic acoustic echo cancellation (SAEC), depending on the room impulse response and acoustic path where the cancellation is performed. However, a large tap-length results in slow convergence and increases the complexity of the tapped delay line structure for FIR adaptive filters. To overcome this problem, there is a need for an optimum tap-length-estimation algorithm that provides better convergence for the adaptive filters used in SAEC. This paper presents a solution to the problem of balancing convergence and steady-state performance of long length adaptive filters used for SAEC by proposing a new tap-length-optimization algorithm. The optimum tap length and step size of the adaptive filter are derived considering an impulse response with an exponentially-decaying envelope, which models a wide range of acoustic echo paths. The tap-length optimization is applied to a single long adaptive filter with thousands of coefficients to decrease the total number of weights, which in turn reduces the computational load. To further increase the convergence rate, the proposed tap-length-optimization algorithm is applied to an existing multiple sub-filter-based echo canceller, for which we present a convergence analysis. Computer simulations are also presented, comparing the proposed approach with related work.