Acoustic Echo Cancellation Algorithms Research Articles

Acoustic Echo Cancellation Algorithm Based on Kalman Filtering of Skewed Observation Noise

Acoustic Echo Cancellation Algorithm Based on Kalman Filtering of Skewed Observation Noise

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.

A family of split kernel adaptive filtering algorithms for nonlinear stereophonic acoustic echo cancellation

A family of split kernel adaptive filtering algorithms for nonlinear stereophonic acoustic echo cancellation

Distributed Combined Acoustic Echo Cancellation and Noise Reduction in Wireless Acoustic Sensor and Actuator Networks

The paper presents distributed algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) in a wireless acoustic sensor and actuator network (WASAN) where each node may have multiple microphones and multiple loudspeakers, and where the desired signal is a speech signal. A centralized integrated AEC and NR algorithm, i.e., multichannel Wiener filter (MWF), is used as starting point where echo signals are viewed as background noise signals and loudspeaker signals are used as additional input signals to the algorithm. By including prior knowledge (PK), namely that the loudspeaker signals do not contain any desired signal component, an alternative centralized cascade algorithm (PK-MWF) is obtained with an AEC stage first followed by an MWF-based NR stage which has a lower computational complexity. Distributed algorithms can then be obtained from the MWF and PK-MWF algorithm, i.e., the generalized eigenvalue decomposition (GEVD)-based distributed adaptive node-specific signal estimation (DANSE) and PK-GEVD-DANSE algorithm, respectively. In the former, each node performs a reduced dimensional integrated AEC and NR algorithm and broadcasts only 1 fused signal (instead of all its signals) to the other nodes. In the PK-GEVD-DANSE algorithm, each node performs a reduced dimensional cascade AEC and NR algorithm and broadcasts only 2 fused signals (instead of all its signals) to the other nodes. The distributed algorithms achieve the same performance, upon convergence, as the corresponding centralized integrated (MWF) and centralized cascade (PK-MWF) algorithm. It is observed, however, that the communication cost in the PK-GEVD-DANSE algorithm can also be reduced, where each node then broadcasts only 1 fused signal (instead of 2 signals) to the other nodes. The resulting algorithm, referred to as the pruned PK-GEVD-DANSE (pPK-GEVD-DANSE) algorithm, then effectively combines the lowest possible communication cost (as low as in the GEVD-DANSE algorithm) with a lowest possible computational complexity in each node (further reduced from the PK-GEVD-DANSE computational complexity), within the class of algorithms considered in this paper.

An online algorithm for echo cancellation, dereverberation and noise reduction based on a Kalman-EM Method

Many modern smart devices are equipped with a microphone array and a loudspeaker (or are able to connect to one). Acoustic echo cancellation algorithms, specifically their multi-microphone variants, are essential components in such devices. On top of acoustic echos, other commonly encountered interference sources in telecommunication systems are reverberation, which may deteriorate the desired speech quality in acoustic enclosures, specifically if the speaker distance from the array is large, and noise. Although sub-optimal, the common practice in such scenarios is to treat each problem separately. In the current contribution, we address a unified statistical model to simultaneously tackle the three problems. Specifically, we propose a recursive EM (REM) algorithm for solving echo cancellation, dereverberation and noise reduction. The proposed approach is derived in the short-time Fourier transform (STFT) domain, with time-domain filtering approximated by the convolutive transfer function (CTF) model. In the E-step, a Kalman filter is applied to estimate the near-end speaker, based on the noisy and reveberant microphone signals and the echo reference signal. In the M-step, the model parameters, including the acoustic systems, are inferred. Experiments with human speakers were carried out to examine the performance in dynamic scenarios, including a walking speaker and a moving microphone array. The results demonstrate the efficiency of the echo canceller in adverse conditions together with a significant reduction in reverberation and noise. Moreover, the tracking capabilities of the proposed algorithm were shown to outperform baseline methods.

Deep learning-based stereophonic acoustic echo suppression without decorrelation.

Traditional stereophonic acoustic echo cancellation algorithms need to estimate acoustic echo paths from stereo loudspeakers to a microphone, which often suffers from the nonuniqueness problem caused by a high correlation between the two far-end signals of these stereo loudspeakers. Many decorrelation methods have already been proposed to mitigate this problem. However, these methods may reduce the audio quality and/or stereophonic spatial perception. This paper proposes to use a convolutional recurrent network (CRN) to suppress the stereophonic echo components by estimating a nonlinear gain, which is then multiplied by the complex spectrum of the microphone signal to obtain the estimated near-end speech without a decorrelation procedure. The CRN includes an encoder-decoder module and two-layer gated recurrent network module, which can take advantage of the feature extraction capability of the convolutional neural networks and temporal modeling capability of recurrent neural networks simultaneously. The magnitude spectra of the two far-end signals are used as input features directly without any decorrelation preprocessing and, thus, both the audio quality and stereophonic spatial perception can be maintained. The experimental results in both the simulated and real acoustic environments show that the proposed algorithm outperforms traditional algorithms such as the normalized least-mean square and Wiener algorithms, especially in situations of low signal-to-echo ratio and high reverberation time RT60.

Multichannel speaker interference reduction using frequency domain adaptive filtering

Microphone leakage or crosstalk is a common problem in multichannel close-talk audio recordings (e.g., meetings or live music performances), which occurs when a target signal does not only couple into its dedicated microphone, but also in all other microphone channels. For further signal processing such as automatic transcription of a meeting, a multichannel speaker interference reduction is required in order to eliminate the interfering speech signals in the microphone channels. The contribution of this paper is twofold: First, we consider multichannel close-talk recordings of a three-person meeting scenario with various different crosstalk levels. In order to eliminate the crosstalk in the target microphone channel, we extend a multichannel Wiener filter approach, which considers all individual microphone channels. Therefore, we integrate an adaptive filter method, which was originally proposed for acoustic echo cancellation (AEC), in order to obtain a well-performing interferer (noise) component estimation. This results in an improved speech-to-interferer ratio by up to 2.7 dB at constant or even better speech component quality. Second, since an AEC method requires typically clean reference channels, we investigate and report findings why the AEC algorithm is able to successfully estimate the interfering signals and the room impulse responses between the microphones of the interferer and the target speakers even though the reference signals are themselves disturbed by crosstalk in the considered meeting scenario.

A nonparametric variable step-size subband adaptive filtering algorithm for acoustic echo cancellation

Acoustic echo cancellation is often applied in communication and video call system to reduce unnecessary echoes generated between speakers and microphones. In these systems, the speech input signal of the adaptive filter is often colored and unstable, which decays the convergence rate of the adaptive filter if the NLMS algorithm is used. In this paper, an improved nonparametric variable step-size subband (NPVSS-NSAF) algorithm is proposed to address the problem. The variable step-size is derived by minimizing the sum of the square Euclidean norm of the difference between the optimal weight vectors to be updated and the past estimated weight vectors. Then the parameters are eliminated by using the power of subband signal noise equal to the power of subband posteriori error. The performance of the proposed algorithm is simulated in the aspects of misalignment and return loss enhancement. Experiment results show a fast convergence rate and low misalignment of the proposed algorithm in system identification. Keywords: echo cancellation, fast convergence, low misalignment, nonparametric variable step size DOI: 10.25165/j.ijabe.20201303.5625 Citation: Song Y, Ren Y Z, Liu X L, Gao W L, Tao S, Guo L. A nonparametric variable step-size subband adaptive filtering algorithm for acoustic echo cancellation. Int J Agric & Biol Eng, 2020; 13(3): 168–173.

A novel set membership fast NLMS algorithm for acoustic echo cancellation

In order to improve the performances of the Fast Normalized Least Mean Square type (FNLMS) adaptive filtering algorithm in the context of Acoustic Echo Cancellation (AEC), this work proposes an Improved Set Membership version by exploiting, firstly, the theory of set membership identification (SMI) to the FNLMS algorithm to get more complexity reduction, then by using the estimated output error to update the step size which results to better convergence speed and tracking ability. The obtained algorithm is called the Improved Set Membership FNLMS (ISM-FNLMS) algorithm. Simulation results have demonstrated the superiority and the good performances of the proposed algorithm compared with NLMS, SM-NLMS, FNLMS, SM-FNLMS and SM-NLMS with Robust Error Bound (SMREB-NLMS) algorithms in terms of convergence speed, steady-state mean square filtering error (MSE), tracking capability and computational complexity performances.

Channel Sparsity Aware Function Expansion Filters Using the RLS Algorithm for Nonlinear Acoustic Echo Cancellation

In this paper, we propose a channel sparsity aware sequential recursive least squares (sparse SEQ-RLS) algorithm for function expansion filters with applications in nonlinear echo cancellation. The algorithm is developed based on a diagonal channel structure from the Volterra filter and updating dominant coefficients taking into consideration of sparse elements in the diagonal channel. The third-order Volterra, third-order even mirror Fourier nonlinear (EMFN), and functional link artificial neural network (FLANN) filters are developed according to the sparse SEQ-RLS algorithm. The computation complexity for the upper bound is analyzed to validate the efficiency for each proposed filter. Computer simulation results demonstrate that all proposed function expansion filters with the sparse SEQ-RLS algorithm are effective for nonlinear echo cancellation. In general, the EMFN filter provides better performance compared to the Volterra and FLANN filters.

A New Algorithm for Acoustic Echo Cancellation using Adaptive Filter

The rapid recent growth of technology has changed the whole communications. However, the presence of a large acoustic coupling between the loudspeaker and microphone would produce a loud echo that would make conversation difficult. The echo suppressor offers a simple but effective method to counter the echo problem. However, the echo suppressor can support only half-duplex communication. The full-duplex communication allows both speakers to talk at the same time. This objective of this research is to produce an improved echo cancellation algorithm, which is capable of providing convincing results. The adaptive filters are used to reduce the unwanted echo and increasing the quality of speech signal. This paper focus on different adaptive filters algorithms and characterzied by the NLMS, DTD and NLP, LMS algorithms, and the calculation of different adaptive and echo cancellation algorithms must be simulated utilizing MATLAB.

A Joint Optimized Robust Acoustic Echo Cancellation Algorithm

When the input signals for acoustic echo cancellation (AEC) are related signals, the convergence speed of the traditional normalized least mean square (NLMS) algorithms is significantly reduced. In this paper, a joint optimization robust AEC algorithm is proposed to solve this problem. Based on the analysis of the convergence of the normalized subband adaptive filtering (NSAF) algorithm, the algorithm is optimized by minimizing the mean square error (MSE) of the NSAF algorithm, combining sub-band time-varying step factor and time-varying regularization parameter to update the filter weight vectors. And when the impulse noise occurs, the sub-band cut-off parameter is updated in a recursive manner, which makes the algorithm achieve fast convergence speed and low steady-state error, and has strong robustness to impulse noise. In a series of experiments on AEC, simulation results show that the performance of the algorithm is better than the existing algorithms.

Analyzing the effects of pseudo-optimum tap-length for an MSF-based acoustic echo canceller

Adaptive multiple subfilters (MSF)-based algorithms have gained attention in recent times for their applications in acoustic echo cancellation (AEC). The most advanced combined-error MSF algorithm maintains a trade-off between the different-error and common-error subfilter-based algorithms, and it is employed for echo cancellation applications which need faster convergence. Variable tap-length adaptive filtering algorithms find the optimum filter length and are used for optimizing the tap-length of the subfilters in MSF-based echo cancellers. In case of a long room impulse response, the MSF-based variable tap-length algorithm achieves a pseudo-optimum tap-length, which renders the overall design undermodelled. Therefore, it becomes necessary to analyze the effects of the undermodelled adaptive filter on the performance of the variable tap-length MSF (VT-MSF)-based echo cancellers. In this paper, we present a mathematical investigation of convergence, steady-state MSE, stability and tracking capability of the VT-MSF echo cancellers having deficient tap-length adaptive subfilters. Simulation results are presented in support of the analysis for the VT-MSF algorithm for AEC.

A new adaptive filtering algorithm for stereophonic acoustic echo cancellation

This paper addresses the stereophonic acoustic echo cancelation (SAEC) problem in teleconferencing system by adaptive filtering algorithm. We propose a new stereophonic version of the fast normalized least mean square (FNLMS) algorithm. The proposed algorithm is an extended version of the FNLMS algorithm to the stereophonic case with important improvement of the prediction part that becomes stable in the stereophonic case. The basic idea behind the proposed stereophonic fast normalized least mean square (SFNLMS) algorithm is the normalization of the predictor parameter by the inputs variance for each channel. Simulation results of a comparison between the proposed SFNLMS algorithm and the classical SNLMS version in term of convergence speed and stability are presented.

Blind Signal Separation Algorithm for Acoustic Echo Cancellation

This paper is to the blind signal separation algorithm applied to acoustic echo cancellation. This algorithm doesn’t degrade the performance of echo cancellation even in the double-talk. In the closed echo environment, the mixing model of acoustic signals has multi-channel, so the convolutive blind signal separation method is applied. And the mixing coefficients are computed by using the feedback model without directly calculating the separation coefficients. The coefficient updating is performed by iterative computations based on the second-order statistical properties, thus estimating the near-end speech. Many simulations have been performed to verify the performance of the proposed blind signal separation. Simulation results show that the proposed acoustic echo canceller operates safely regardless of double-talk, and the PESQ is improved by 0.6 point compared with the general adaptive FIR filter structure.

Blind Signal Separation Algorithm for Acoustic Echo Cancellation

This paper is to the blind signal separation algorithm applied to acoustic echo cancellation. This algorithm doesn’t degrade the performance of echo cancellation even in the double-talk. In the closed echo environment, the mixing model of acoustic signals has multi-channel, so the convolutive blind signal separation method is applied. And the mixing coefficients are computed by using the feedback model without directly calculating the separation coefficients. The coefficient updating is performed by iterative computations based on the second-order statistical properties, thus estimating the near-end speech. Many simulations have been performed to verify the performance of the proposed blind signal separation. Simulation results show that the proposed acoustic echo canceller operates safely regardless of double-talk, and the PESQ is improved by 0.6 point compared with the general adaptive FIR filter structure.

Combined distributed incremental affine projection algorithm for acoustic echo cancellation

To improve the performance of the conventional distributed incremental affine projection algorithm (DIAPA) in acoustic echo cancellation, a combined distributed incremental affine projection algorithm is proposed in this brief. The proposed algorithm combines two DIAPA equipped with different projection orders by employing a mixing scalar parameter, where one with high projection order guarantees fast convergence rate for the initial stage and the other with low projection order maintains small misalignment for the steady stage. The mixing scalar parameter is endowed with capability to adapt itself to be close to one in the initial stage and approximate to zero in the steady-state stage. Moreover, the analysis of the mixing scalar parameter is presented. We also take into account the computational complexity of the proposed algorithm. The simulations conducted in the system identification and acoustic echo cancellation experiments illustrate the superiority of our findings.

Set‐membership improved normalised subband adaptive filter algorithms for acoustic echo cancellation

In order to improve the performances of recently-presented improved normalized subband adaptive filter (INSAF) and proportionate INSAF algorithms for highly noisy system, this paper proposes their set-membership versions by exploiting the theory of set-membership filtering. Apart from obtaining smaller steady-state error, the proposed algorithms significantly reduce the overall computational complexity. In addition, to further improve the steady-state performance for the algorithms, their smooth variants are developed by using the smoothed absolute subband output errors to update the step sizes. Simulation results in the context of acoustic echo cancellation have demonstrated the superiority of the proposed algorithms.

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.

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.