Time-Reversal Approach to the Stereophonic Acoustic Echo Cancellation Problem

Nguyen, Dinh-Quy; Gan, Woon‐Seng; Khong, Andy W. H.

doi:10.1109/tasl.2010.2048941

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…In this approach, instead of driving the loudspeakers with signals xi(k), distorted signalsxi(k) are used as the input of the acoustic system and the adaptive filters. The two major branches of preprocessing techniques are nonlinear [2], [4], [3], [23] and time-variant [24], [25], [26], [27] preprocessing, which are both able to effectively decorrelate the system excitation. Introduced distortions, however, reduce the perceptual quality of the displayed signals which gives rise to research in psychoacoustically motivated [28], [29] decorrelation techniques.…”

Section: A Decorrelation Preprocessingmentioning

confidence: 99%

Trends in adaptive MISO system identification for multichannel audio reproduction and speech communication

Thune

Enzner

2013

2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA)

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Online identification of multiple-input / single-output (MISO) acoustic systems is one of the long-standing and continuing challenges in multichannel speech and audio applications. Fast and robust estimation of the impulse response of an acoustic system is a key requirement for several adaptive solutions in time-varying scenarios, such as stereophonic acoustic echo cancellation, room equalization, or crosstalk cancellation. The inevitable presence of cross-correlated loudspeaker signals that is implied by multichannel applications, however, entails the well-known non-uniqueness problem of MISO system identification. Apart from this fundamental issue, a more practical problem already consists in the lack of techniques to evaluate the estimated impulse responses properly. Since well-established measures are often not capable of accounting for all aspects of online MISO system identification, we revert to the recently proposed spectral-importance weighted misalignment (SIWM) to assess MISO identification. In this contribution, we review SIWM and its relation to well-established evaluation tools. On this basis, we provide an insight into the problem of MISO system identification in applications driven by real stereo data. We also analyze and compare a traditional and a very recent approach to deal with the non-uniqueness problem.

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Section: A Decorrelation Preprocessingmentioning

confidence: 99%

Trends in adaptive MISO system identification for multichannel audio reproduction and speech communication

Thune

Enzner

2013

2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA)

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“…A widely used solution for reducing the effect of this problem is the acoustic feedback cancellation (AFC) and acoustic echo cancellation (AEC) algorithms which identify the acoustic feedback/echo paths by means of an adaptive filter in a system identification configuration, see e.g., [4]- [10]. Fig.…”

Section: Analysis Of Acoustic Feedback/echo Cancellation In Multiple-mentioning

confidence: 99%

“…There are different ways to model these variations, see e.g., [14]. In this work, we use a simple random walk model given by (5) for the th feedback/echo path, where is a zeromean Gaussian stochastic sequence with covariance (6) In the time domain, the feedback/echo path variation vector is (7) The correlation matrix of the th and th feedback/echo path variation is defined as (8) The adaptively estimated feedback/echo path of order is expressed by (9) and the estimation error vector which expresses the difference between the true and estimated feedback/echo path is (10) with a frequency response given by (11) In the analysis, we consider the loudspeaker signal as a deterministic zero-mean signal, because it is measurable and thereby known with certainty. However, our results remain valid, even if the loudspeaker signal is considered as a realization of a stochastic process; the same approach is applied and explained in details in [27].…”

Section: System Descriptionmentioning

confidence: 99%

“…Using (22), (15), (14), and (7), the estimation error vector defined in (10) can also be expressed by (23) where is the identity matrix. Assuming the feedback/echo path variation vector is uncorrelated with every other term in (23), we introduce the matrix and compute the estimation error correlation matrix as (24) Under the assumption of sufficiently small step size , in principle, , it follows that .…”

Section: A Ptf For Lms Algorithmmentioning

confidence: 99%

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Analysis of Acoustic Feedback/Echo Cancellation in Multiple-Microphone and Single-Loudspeaker Systems Using a Power Transfer Function Method

Guo

Elmedyb

Jensen

et al. 2011

IEEE Trans. Signal Process.

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In this work, we analyze a general multiple-microphone and single-loudspeaker audio processing system, where a multichannel adaptive system is used to cancel the effect of acoustic feedback/echo, and a beamformer processes the feedback/echo canceled signals. We introduce and derive an accurate approximation of a frequency domain measure-the power transfer function-and show how it can be used to predict the convergence rate, system stability bound and the steady-state behavior of the entire cancellation system across frequency and time. We consider three example adaptive algorithms in the cancellation system: the least mean square, normalized least mean square, and the recursive least squares algorithms. Furthermore, we derive expressions to determine the step size parameter in the adaptive algorithms to achieve a desired system behavior, e.g., convergence rate at a specific frequency. Finally, we compare and discuss the performance of all three adaptive algorithms, and we verify the derived expressions through simulation experiments.Index Terms-Adaptive echo cancellation, adaptive feedback cancellation, convergence rate, steady-state behavior.

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“…MAEC may be seen as a straightforward generalization of the monophonic acoustic echo cancellation (AEC), but it entails more problems to tackle, such as nonuniqueness and slow convergence due to inter-channel correlation, and the poor ability to react to changes in the environmental conditions [5]. Such issues have roused remarkable interest over the years [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

Convex combination of MIMO filters for multichannel acoustic echo cancellation

Comminiello

Scardapane

Scarpiniti

et al. 2013

2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA)

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This paper introduces a new framework for multichannel adaptive filtering, aiming at improving performance of an overall filtering system. The proposed architecture relies on the properties of the adaptive combination of filters which exploits the capabilities of different constituents, thus adaptively providing at least the behaviour of the best performing filter. Applying this concept to multichannel filtering systems, we define a scheme for the combination of multiple-input multiple-output (MIMO) filters. More precisely the proposed structure involves the combination of two different multiple-input singleoutput (MISO) systems for each MIMO output. We propose such framework with application to the multichannel acoustic echo cancellation (MAEC) with the goal of giving robustness to the system against impulsive background noise, and thus improving overall cancelling performance. Experimental results show the effectiveness of the proposed combined MAEC in the presence of adverse environmental conditions. © 2013 University of Trieste and University of Zagreb

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Time-Reversal Approach to the Stereophonic Acoustic Echo Cancellation Problem

Cited by 12 publications

References 27 publications

Trends in adaptive MISO system identification for multichannel audio reproduction and speech communication

Trends in adaptive MISO system identification for multichannel audio reproduction and speech communication

Analysis of Acoustic Feedback/Echo Cancellation in Multiple-Microphone and Single-Loudspeaker Systems Using a Power Transfer Function Method

Convex combination of MIMO filters for multichannel acoustic echo cancellation

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