The Experts below are selected from a list of 3831 Experts worldwide ranked by ideXlab platform
Walter Kellermann - One of the best experts on this subject based on the ideXlab platform.
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nonlinear acoustic Echo Cancellation using elitist resampling particle filter
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Mhd Modar Halimeh, Christian Huemmer, Walter KellermannAbstract:This paper considers an effective method for nonlinear acoustic Echo Cancellation (NL-AEC). More specifically, we model the nonlinear Echo path by a latent state vector capturing the coefficients of a memoryless processor and a linear finite impulse response filter. To estimate the posterior probability distribution of the state vector, an elitist particle filter based on evolutionary strategies (EPFES) has been proposed, which evaluates realizations of the latent state vector based on long-term fitness measures. This method includes a manually-tuned recursive calculation of the probabilities that the observation has been produced by the state-vector realizations. For avoiding this manual tuning, we introduce a new approach denoted as Elitist Resampling Particle Filtering (ERPF) which can also be shown to combine the advantages of the Sequential Importance Sampling Particle Filter (SIS-PF) and the Sequential Importance Sampling/Resampling Particle Filter (SIR-PF). This new approach allows universal use and leads to superior system identification performance compared to both the original EPFES as well as the SIR-PF, as verified for a simulated scenario and a real smartphone recording.
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Significance-aware filtering for nonlinear acoustic Echo Cancellation
EURASIP Journal on Advances in Signal Processing, 2016Co-Authors: Christian Hofmann, Christian Huemmer, Michael Guenther, Walter KellermannAbstract: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.
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Orthogonalized power filters for nonlinear acoustic Echo Cancellation
Signal Processing, 2006Co-Authors: F Kuech, Walter KellermannAbstract:Standard approaches for the Cancellation of acoustic Echoes in telecommunication systems assume that the Echo path to be identified can be modeled by a linear system. However, in applications such as Echo Cancellation for mobile communication terminals, non-negligible nonlinear distortions are introduced by loudspeakers and their amplifiers, resulting in a significant degradation of the performance of purely linear approaches. In this contribution we consider so-called power filters in order to cope with these kinds of nonlinear Echo paths. By combining time-domain orthogonalization methods with a DFT-domain implementation of power filters we derive corresponding quasi-complete orthogonalized versions. As the statistical properties of speech input are non-stationary, the orthogonalization must follow this time-variance, too. Furthermore, we introduce a step-size control for the adaptation of the equivalent orthogonalized structure. Experiments with real hardware show that, with the proposed nonlinear approach, an increase in Echo attenuation can be achieved, if the loudspeaker system introduces saturation-type nonlinearities in the Echo path.
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nonlinear acoustic Echo Cancellation using adaptive orthogonalized power filters
International Conference on Acoustics Speech and Signal Processing, 2005Co-Authors: F Kuech, A Mitnacht, Walter KellermannAbstract:In acoustic Echo Cancellation as, e.g., for mobile communication receivers, loudspeakers and their amplifiers cause significant nonlinear distortion in the Echo path, resulting in a degradation of the performance of linear Echo cancelers. In order to cope with this type of nonlinear Echo path, we discuss an orthogonalized version of power filter that can be considered as a parallelized realization of the cascade of a memoryless polynomial followed by a linear filter. As, in the Echo Cancellation context, the statistics of the speech input are non-stationary and not known in advance, the orthogonalization follows the signal statistics. The performance of the resulting novel nonlinear structure is evaluated by experiments using real hardware.
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nonlinear acoustic Echo Cancellation with fast converging memoryless preprocessor
International Conference on Acoustics Speech and Signal Processing, 2000Co-Authors: Alexander Stenger, Walter KellermannAbstract:Low-cost audio components in hands-free telephone applications call for nonlinear adaptive Echo Cancellation (AEC). It has been demonstrated that a cascade of a polynomial and an FIR filter can cancel such nonlinear Echoes (Stenger and Rabenstein 1998). Another technique employs a hard-clipping curve with LMS adapted saturation parameter (Nollet and Jones 1997). For both cascaded systems we derive an LMS-type adaptation using a common framework, and propose stepsize normalizations for both approaches. To achieve sufficiently fast convergence for practical use, an RLS-type adaptation for the polynomial is derived and experimentally verified. Both techniques are compared using real hardware and speech signals, and show robust convergence behaviour and an Echo reduction gain of up to 10 dB compared to a linear AEC.
Silviu Ciochina - One of the best experts on this subject based on the ideXlab platform.
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Robust general Kalman filter for Echo Cancellation
2016Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:Abstract—The Kalman filter is a very interesting signal pro-cessing tool, which is widely used in many practical applications. In this paper, we study the Kalman filter in the context of Echo Cancellation. The contribution of this work is threefold. First, we derive a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach. Second, we show how this general Kalman filter (GKF) is connected with some of the most popular adaptive filters for Echo Cancellation, i.e., the normalized least-mean-square (NLMS) algorithm, the affine projection algorithm (APA) and its proportionate version (PAPA). Third, a simplified Kalman filter is developed in order to reduce the computational load of the GKF; this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the proposed algorithms, which can be attractive choices for Echo Cancellation. Index Terms—Echo Cancellation, Kalman filter, adaptive filters, recursive least-squares (RLS) algorithm, affine projec-tion algorithm (APA), proportionate APA (PAPA), normalized least-mean-square (NLMS) algorithm. I
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A FAMILY OF VARIABLE STEP-SIZE NLMS ALGORITHMS FOR Echo Cancellation
2016Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The normalized least-mean-square (NLMS) algorithm is one of the most common choices for Echo Cancellation. Nevertheless, an NLMS algorithm has to compromise between several performances criteria (e.g., convergence rate versus misadjustment, tracking capabilities versus robustness). Thus, a variable step-size NLMS (VSS-NLMS) algorithm represents a more reliable solution. Recently, several VSS-NLMS algorithms that take into account the existence of the near-end signal (in terms of power estimate) have been proposed with the objective of recovering the near-end signal from the error signal. Since this is the basic goal in Echo Cancellation, this class of VSS-NLMS algorithms can be very suitable for such an application. The main issue remains the estimation of the near-end signal power, in terms of accuracy and other practical aspects (e.g., available parameters, computational complexity). This paper analyzes different solutions for this problem, making a first unified approach over the performances of this family of VSS-NLMS algorithms. 1
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a kalman filter with individual control factors for Echo Cancellation
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Steven L GrantAbstract:In Echo Cancellation, the main goal is to recover the near-end signal from the error signal of the adaptive filter, which identifies the Echo path. In this context, the Kalman filter represents a very appealing choice, since its basic criterion follows the minimization of the system misalignment (instead of the usual error-based cost function). In this paper, we propose a Kalman filter with individual control factors, in terms of using a different level of uncertainty for each coefficient of the filter. As compared to the basic Kalman filter (which imposes the same uncertainty for all the coefficients of the impulse response), the proposed algorithm achieves better performance, especially in terms of the steady-state misalignment.
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sparse adaptive filters for Echo Cancellation
2010Co-Authors: Constantin Paleologu, Silviu CiochinaAbstract:Abstract Adaptive filters with a large number of coefficients are usually involved in both network and acoustic Echo Cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the Echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for Echo Cancellation. Besides a comprehensive review of the basic proportionate-type algorithms, we also present some of the latest developments in the field and propose some new solutions for further performance improvement, e.g., variable step-size versions and novel proportionate-type affine projection algorithms. An experimental study is also provided in order to compare many sparse adaptive filters in different Echo canc...
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robust variable step size affine projection algorithm suitable for acoustic Echo Cancellation
European Signal Processing Conference, 2008Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The affine projection algorithm (APA) and different versions of it have proved to be very attractive choices for acoustic Echo Cancellation (AEC). In this context, a classical APA with a constant step-size has to compromise between two performance criteria, i.e., 1) high convergence rates and good tracking capabilities, and 2) low misadjustment and robustness against background noise variations and double-talk. Consequently, a variable step-size APA (VSS-APA) is a more reliable solution. In this paper we propose a VSS-APA that is designed to recover the near-end signal from the error signal of the adaptive filter. Therefore, it is robust against near-end signal variations, including double-talk. Moreover, since it does not require a priori information about the acoustic environment, the proposed algorithm is easy to control in real-world AEC applications.
Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.
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Robust general Kalman filter for Echo Cancellation
2016Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:Abstract—The Kalman filter is a very interesting signal pro-cessing tool, which is widely used in many practical applications. In this paper, we study the Kalman filter in the context of Echo Cancellation. The contribution of this work is threefold. First, we derive a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach. Second, we show how this general Kalman filter (GKF) is connected with some of the most popular adaptive filters for Echo Cancellation, i.e., the normalized least-mean-square (NLMS) algorithm, the affine projection algorithm (APA) and its proportionate version (PAPA). Third, a simplified Kalman filter is developed in order to reduce the computational load of the GKF; this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the proposed algorithms, which can be attractive choices for Echo Cancellation. Index Terms—Echo Cancellation, Kalman filter, adaptive filters, recursive least-squares (RLS) algorithm, affine projec-tion algorithm (APA), proportionate APA (PAPA), normalized least-mean-square (NLMS) algorithm. I
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A FAMILY OF VARIABLE STEP-SIZE NLMS ALGORITHMS FOR Echo Cancellation
2016Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The normalized least-mean-square (NLMS) algorithm is one of the most common choices for Echo Cancellation. Nevertheless, an NLMS algorithm has to compromise between several performances criteria (e.g., convergence rate versus misadjustment, tracking capabilities versus robustness). Thus, a variable step-size NLMS (VSS-NLMS) algorithm represents a more reliable solution. Recently, several VSS-NLMS algorithms that take into account the existence of the near-end signal (in terms of power estimate) have been proposed with the objective of recovering the near-end signal from the error signal. Since this is the basic goal in Echo Cancellation, this class of VSS-NLMS algorithms can be very suitable for such an application. The main issue remains the estimation of the near-end signal power, in terms of accuracy and other practical aspects (e.g., available parameters, computational complexity). This paper analyzes different solutions for this problem, making a first unified approach over the performances of this family of VSS-NLMS algorithms. 1
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Stereophonic acoustic Echo Cancellation: Analysis of the misalignment in the frequency domain
2016Co-Authors: Andy W H Khong, Jacob Benesty, Senior Member, Patrick A NaylorAbstract:Abstract—The performance in terms of misalignment of adap-tive algorithms, in general, is dependent on the conditioning of the input signal covariance matrix. The performance of two-channel adaptive algorithms is further degraded by the high interchannel coherence between the two input signals. In this letter, we establish the relationship between interchannel coherence of the two input signals and condition of the corresponding covariance matrix for stereo acoustic Echo Cancellation application. We show how this re-lationship affects the misalignment of a frequency-domain adaptive algorithm. We provide simulation results for both white Gaussian noise and speech input to verify our mathematical analysis. Index Terms—Condition number, interchannel coherence, mis-alignment, stereophonic acoustic Echo Cancellation
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a kalman filter with individual control factors for Echo Cancellation
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Steven L GrantAbstract:In Echo Cancellation, the main goal is to recover the near-end signal from the error signal of the adaptive filter, which identifies the Echo path. In this context, the Kalman filter represents a very appealing choice, since its basic criterion follows the minimization of the system misalignment (instead of the usual error-based cost function). In this paper, we propose a Kalman filter with individual control factors, in terms of using a different level of uncertainty for each coefficient of the filter. As compared to the basic Kalman filter (which imposes the same uncertainty for all the coefficients of the impulse response), the proposed algorithm achieves better performance, especially in terms of the steady-state misalignment.
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robust variable step size affine projection algorithm suitable for acoustic Echo Cancellation
European Signal Processing Conference, 2008Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The affine projection algorithm (APA) and different versions of it have proved to be very attractive choices for acoustic Echo Cancellation (AEC). In this context, a classical APA with a constant step-size has to compromise between two performance criteria, i.e., 1) high convergence rates and good tracking capabilities, and 2) low misadjustment and robustness against background noise variations and double-talk. Consequently, a variable step-size APA (VSS-APA) is a more reliable solution. In this paper we propose a VSS-APA that is designed to recover the near-end signal from the error signal of the adaptive filter. Therefore, it is robust against near-end signal variations, including double-talk. Moreover, since it does not require a priori information about the acoustic environment, the proposed algorithm is easy to control in real-world AEC applications.
Constantin Paleologu - One of the best experts on this subject based on the ideXlab platform.
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Robust general Kalman filter for Echo Cancellation
2016Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:Abstract—The Kalman filter is a very interesting signal pro-cessing tool, which is widely used in many practical applications. In this paper, we study the Kalman filter in the context of Echo Cancellation. The contribution of this work is threefold. First, we derive a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach. Second, we show how this general Kalman filter (GKF) is connected with some of the most popular adaptive filters for Echo Cancellation, i.e., the normalized least-mean-square (NLMS) algorithm, the affine projection algorithm (APA) and its proportionate version (PAPA). Third, a simplified Kalman filter is developed in order to reduce the computational load of the GKF; this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the proposed algorithms, which can be attractive choices for Echo Cancellation. Index Terms—Echo Cancellation, Kalman filter, adaptive filters, recursive least-squares (RLS) algorithm, affine projec-tion algorithm (APA), proportionate APA (PAPA), normalized least-mean-square (NLMS) algorithm. I
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A FAMILY OF VARIABLE STEP-SIZE NLMS ALGORITHMS FOR Echo Cancellation
2016Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The normalized least-mean-square (NLMS) algorithm is one of the most common choices for Echo Cancellation. Nevertheless, an NLMS algorithm has to compromise between several performances criteria (e.g., convergence rate versus misadjustment, tracking capabilities versus robustness). Thus, a variable step-size NLMS (VSS-NLMS) algorithm represents a more reliable solution. Recently, several VSS-NLMS algorithms that take into account the existence of the near-end signal (in terms of power estimate) have been proposed with the objective of recovering the near-end signal from the error signal. Since this is the basic goal in Echo Cancellation, this class of VSS-NLMS algorithms can be very suitable for such an application. The main issue remains the estimation of the near-end signal power, in terms of accuracy and other practical aspects (e.g., available parameters, computational complexity). This paper analyzes different solutions for this problem, making a first unified approach over the performances of this family of VSS-NLMS algorithms. 1
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a kalman filter with individual control factors for Echo Cancellation
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Constantin Paleologu, Silviu Ciochina, Jacob Benesty, Steven L GrantAbstract:In Echo Cancellation, the main goal is to recover the near-end signal from the error signal of the adaptive filter, which identifies the Echo path. In this context, the Kalman filter represents a very appealing choice, since its basic criterion follows the minimization of the system misalignment (instead of the usual error-based cost function). In this paper, we propose a Kalman filter with individual control factors, in terms of using a different level of uncertainty for each coefficient of the filter. As compared to the basic Kalman filter (which imposes the same uncertainty for all the coefficients of the impulse response), the proposed algorithm achieves better performance, especially in terms of the steady-state misalignment.
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sparse adaptive filters for Echo Cancellation
2010Co-Authors: Constantin Paleologu, Silviu CiochinaAbstract:Abstract Adaptive filters with a large number of coefficients are usually involved in both network and acoustic Echo Cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the Echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for Echo Cancellation. Besides a comprehensive review of the basic proportionate-type algorithms, we also present some of the latest developments in the field and propose some new solutions for further performance improvement, e.g., variable step-size versions and novel proportionate-type affine projection algorithms. An experimental study is also provided in order to compare many sparse adaptive filters in different Echo canc...
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robust variable step size affine projection algorithm suitable for acoustic Echo Cancellation
European Signal Processing Conference, 2008Co-Authors: Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The affine projection algorithm (APA) and different versions of it have proved to be very attractive choices for acoustic Echo Cancellation (AEC). In this context, a classical APA with a constant step-size has to compromise between two performance criteria, i.e., 1) high convergence rates and good tracking capabilities, and 2) low misadjustment and robustness against background noise variations and double-talk. Consequently, a variable step-size APA (VSS-APA) is a more reliable solution. In this paper we propose a VSS-APA that is designed to recover the near-end signal from the error signal of the adaptive filter. Therefore, it is robust against near-end signal variations, including double-talk. Moreover, since it does not require a priori information about the acoustic environment, the proposed algorithm is easy to control in real-world AEC applications.
Peter Vary - One of the best experts on this subject based on the ideXlab platform.
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a psychoacoustic approach to combined acoustic Echo Cancellation and noise reduction
IEEE Transactions on Speech and Audio Processing, 2002Co-Authors: Stefan Gustafsson, Rainer Martin, Peter Jax, Peter VaryAbstract:This paper presents and compares algorithms for combined acoustic Echo Cancellation and noise reduction for hands-free telephones. A structure is proposed, consisting of a conventional acoustic Echo canceler and a frequency domain postfilter in the sending path of the hands-free system. The postfilter applies the spectral weighting technique and attenuates both the background noise and the residual Echo which remains after imperfect Echo Cancellation. Two weighting rules for the postfilter are discussed. The first is a conventional one, known from noise reduction, which is extended to attenuate residual Echo as well as noise. The second is a psychoacoustically motivated weighting rule. Both rules are evaluated and compared by instrumental and auditive tests. They succeed about equally well in attenuating the noise and the residual Echo. In listening tests, however, the psychoacoustically motivated weighting rule is mostly preferred since it leads to more natural near end speech and to less annoying residual noise.
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partitioned residual Echo power estimation for frequency domain acoustic Echo Cancellation and postfiltering
European Transactions on Telecommunications, 2002Co-Authors: Gerald Enzner, Rainer Martin, Peter Vary, Roland Auteur Du Texte MartinAbstract:Residual Echo arises in hands-free telephony equipment due to insufficient Echo canceler convergence, but can be suppressed using a postfilter. The residual Echo power spectral density is the most crucial control parameter for both frequency-domain acoustic Echo Cancellation and combined residual Echo and noise postfiltering. In this contribution we present and compare residual Echo power spectral estimation techniques. We introduce a new partitioned block-adaptive estimation technique delivering considerably improved residual Echo estimates in strongly reverberant and noisy acoustic environments. We show that the adaptation loop of the frequency-domain adaptive filter (FDAF) can be used simultaneously for residual Echo power estimation and tracking of the Echo path impulse response. In this way, the FDAF and the postfilter concept supplement each other in a true synergy with low complexity. The resulting Echo and noise control system proves to be robust in double talk situations as well.
