Herbordt W, Buchner H, Nakamura S, Kellermann W (2007)
Publication Language: English
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Book Volume: 15
Pages Range: 1340-1351
Article Number: 4156198
Journal Issue: 4
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 M-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 © 2007 IEEE.
APA:
Herbordt, W., Buchner, H., Nakamura, S., & Kellermann, W. (2007). Multichannel bin-wise robust frequency-domain adaptive filtering and its application to adaptive beamforming. IEEE Transactions on Audio Speech and Language Processing, 15(4), 1340-1351. https://doi.org/10.1109/TASL.2007.894530
MLA:
Herbordt, Wolfgang, et al. "Multichannel bin-wise robust frequency-domain adaptive filtering and its application to adaptive beamforming." IEEE Transactions on Audio Speech and Language Processing 15.4 (2007): 1340-1351.
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