Adaptive filtering in the frequency domain

Dentino, M.; McCool, J.M.; Widrow, Bernard

doi:10.1109/proc.1978.11177

Cited by 232 publications

(60 citation statements)

References 4 publications

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“…The Fast Block LMS (FBLMS) algorithm was proposed to reduce the processing time 121 (Dentino, McCool et al 1978) and as such is more suitable for online diagnostics 122…”

Section: Signal Separation Algorithms 109mentioning

confidence: 99%

Bearing Signal Separation Enhancement with Application to a Helicopter Transmission System

2017

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“…The Fast Block LMS (FBLMS) algorithm was proposed to reduce the processing time 121 (Dentino, McCool et al 1978) and as such is more suitable for online diagnostics 122…”

Section: Signal Separation Algorithms 109mentioning

confidence: 99%

Bearing Signal Separation Enhancement with Application to a Helicopter Transmission System

2017

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“…The generalized frequency-domain adaptive filtering (GFDAF) algorithm has been shown to largely overcome this problem while retaining computational efficiency. The GFDAF algorithm was first presented in [39], being inspired by [30,40] and incorporating concepts of [31,41,42]. Note that the Kalman filter-based approaches have also been generalized for the efficient identification of multiple-input/multiple-output systems [43].…”

Section: Introductionmentioning

confidence: 99%

The generalized frequency-domain adaptive filtering algorithm as an approximation of the block recursive least-squares algorithm

Schneider

Kellermann

2016

EURASIP J. Adv. Signal Process.

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Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To implement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized frequency-domain adaptive filtering (GFDAF) algorithm are required for satisfactory convergence behavior. In this paper, the GFDAF algorithm is rigorously derived as an approximation of the block recursive least-squares (RLS) algorithm. Thereby, the original formulation of the GFDAF algorithm is generalized while avoiding an error that has been in the original derivation. The presented algorithm formulation is applied to pruned transform-domain loudspeaker-enclosure-microphone models in a mathematically consistent manner. Such pruned models have recently been proposed to cope with the tremendous computational demands of massive multichannel AEC. Beyond its generalization, a regularization of the GFDAF is shown to have a close relation to the well-known block least-mean-squares algorithm.

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“…In recent years, adaptation algorithms in the discrete Fourier transform (DFT) domain [frequency-domain adaptive filters (FDAFs)] [19]- [25] became very attractive for acoustic echo cancellation [26]- [32] and for adaptive beamforming [33]- [35] since they 1) combine fast convergence with low computational complexity; 2) can be realized such that sufficiently high tracking capability and sufficiently low delay are obtained; and 3) generalize well to the multichannel (MC) case (MC-FDAF) [27]- [29], [31]. Similar to RLS algorithms, the convergence speed of FDAFs is nearly independent of the condition number of the cross-correlation matrix of the input signals.…”

Section: Introductionmentioning

confidence: 99%

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

Herbordt

Buchner

Nakamura

et al. 2007

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Least-squares error (LSE) or mean-squared error (MSE) optimization criteria lead to adaptive filters that are highly sensitive to impulsive noise. The sensitivity to noise bursts increases with the convergence speed of the adaptation algorithm and limits the performance of signal processing algorithms, especially when fast convergence is required, as for example, in adaptive beamforming for speech and audio signal acquisition or acoustic echo cancellation. In these applications, noise bursts are frequently due to undetected double-talk. In this paper, we present impulsive noise robust multichannel frequency-domain adaptive filters (MC-FDAFs) based on outlier-robust -estimation using a Newton algorithm and a discrete Newton algorithm, which are especially designed for frequency bin-wise adaptation control. Bin-wise adaptation and control in the frequency-domain enables the application of the outlier-robust MC-FDAFs to a generalized sidelobe canceler (GSC) using an adaptive blocking matrix for speech and audio signal acquisition. It is shown that the improved robustness leads to faster convergence and to higher interference suppression relative to nonrobust adaptation algorithms, especially during periods of strong interference.

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Adaptive filtering in the frequency domain

Cited by 232 publications

References 4 publications

Bearing Signal Separation Enhancement with Application to a Helicopter Transmission System

Bearing Signal Separation Enhancement with Application to a Helicopter Transmission System

The generalized frequency-domain adaptive filtering algorithm as an approximation of the block recursive least-squares algorithm

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

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