Multichannel Bin-Wise Robust Frequency-Domain Adaptive Filtering and Its Application to Adaptive Beamforming

Herbordt, Wolfgang; Buchner, Herbert; Nakamura, Satoshi; Kellermann, Walter

doi:10.1109/tasl.2007.894530

Cited by 22 publications

(6 citation statements)

References 47 publications

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“…Good choices may lead reduced computational complexity [38, p. 31]. Also, robust GSC implementations with an adaptive bock matrix have been proposed to account for small changes in the desired signal DOA [15], [46]- [49]. Therefore, it is of interest to study the behavior of the GSC form of the BF-AEC structure.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of a Jointly Optimized Beamformer-Assisted Acoustic Echo Canceler

Maruo

Bermudez

Resende

2014

IEEE Trans. Signal Process.

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This work presents a statistical analysis of a class of jointly optimized beamformer-assisted acoustic echo cancelers (AEC) with the beamformer (BF) implemented in the Generalized Sidelobe Canceler (GSC) form and using the least-mean square (LMS) algorithm. The analysis considers the possibility of independent convergence control for the BF and the AEC. The resulting models permit the study of system performance under typical handling of double-talk and channel changes. We show that the joint optimization of the BF-AEC is equivalent to a linearly-constrained minimum variance problem. Hence, the derived analytical model can be used to predict the transient performance of general adaptive wideband beamformers. We study the transient and steady-state behaviors of the residual mean echo power for stationary Gaussian inputs. A convergence analysis leads to stability bounds for the step-size matrix and design guidelines are derived from the analytical models. Monte Carlo simulations illustrate the accuracy of the theoretical models and the applicability of the proposed design guidelines. Examples include operation under mild degrees of nonstationarity. Finally, we show how a high convergence rate can be achieved using a quasi-Newton adaptation scheme in which the step-size matrix is designed to whiten the combined input vector.Index Terms-Acoustic echo cancellation, adaptive filtering, beamforming, generalized sidelobe canceller, statistical analysis

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis of a Jointly Optimized Beamformer-Assisted Acoustic Echo Canceler

Maruo

Bermudez

Resende

2014

IEEE Trans. Signal Process.

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“…However, these methods do not handle dereverberation problems. Herbordt, Buchner, Nakamura, and Kellermann (2007) integrated echo cancellation with generalized side-lobe cancellation (Hoshuyama, Sugiyama, & Hirano, 1999) in a multichannel microphone array, thereby achieving robust separation of known sources, and emphasized target speech. However, this requires a rough location of the targeted sound source, so it does not satisfy our requirements.…”

Section: Echo Cancellationmentioning

confidence: 99%

Efficient Blind Dereverberation and Echo Cancellation Based on Independent Component Analysis for Actual Acoustic Signals

et al. 2012

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This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT₂₀= 670 ms reverberant environment.

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“…Its output thus provides a noise-reference signal that is useful for parallel processing of the original noisy recording of the target. For example, CFs are used within the blocking matrix part of generalized sidelobe cancellers [1], where the outputs are used for adaptive interference cancellation and post-filtering; see, e.g., [1,2,3]. This paper focuses on the problem of finding efficient CFs.…”

Section: Introductionmentioning

confidence: 99%

“…x R (n) = {h R * s}(n) + y R (n) (2) where n is the time index; * denotes the convolution; x L and x R are, respectively, the signals from the left and right microphones; s is the target signal; and y L and y R are the remaining This work was supported by the Czech Science Foundation through Project No. 14-11898S.…”

Section: Introductionmentioning

confidence: 99%

Sparse target cancellation filters with application to semi-blind noise extraction

Málek

Koldovsky

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Impulse responses of filters that perform spatial null in a target direction, so-called target-cancellation filters (CFs), are usually long and dense due to the reverberant acoustic environment. It is therefore hard to blindly estimate them from noisy recordings of the target. In this paper, we show that efficient sparse CFs having many coefficients equal to zero can be designed such that their cancellation performance is tolerably lower than the performance of dense CFs. We show that an efficient sparse CF can be blindly estimated from noisy data, provided that its support is known. The resulting filter is better than a dense CF which has been blindly estimated without any prior knowledge.

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Multichannel Bin-Wise Robust Frequency-Domain Adaptive Filtering and Its Application to Adaptive Beamforming

Cited by 22 publications

References 47 publications

Statistical Analysis of a Jointly Optimized Beamformer-Assisted Acoustic Echo Canceler

Statistical Analysis of a Jointly Optimized Beamformer-Assisted Acoustic Echo Canceler

Efficient Blind Dereverberation and Echo Cancellation Based on Independent Component Analysis for Actual Acoustic Signals

Sparse target cancellation filters with application to semi-blind noise extraction

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