Robust adaptive beamforming

By H. Cox, R. Zeskind, Mark M. Owen. IEEE Transactions on Acoustics Speech and Signal Processing, 2005

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Comparisons of wavelets, contourlets and curvelets in seismic denoising (2009, positive): “Do (2001) constructed a DFB that avoids modulating the input image and has a simpler rule for expanding the decomposition tree comparing with the original version of DFB (Bamberger and Smith, 1992).”
An analysis of environment, microphone and data simulation mismatches in robust speech recognition (2017, neutral): “The impact of imprecise knowledge of the microphone positions and frequency responses has also been addressed (Cox et al., 1987; Doclo and Moonen, 2007; Anderson et al., 2015).”
Deep Learning for Environmentally Robust Speech Recognition (2017, methodology): “servations as RV V = (1/N) P n Y(n)YH(n) [136]. By then minimising wHRV V w with respect to w subject to the constraint wHd = 1, as mentioned above, the MVDR beamformer ﬁlter coefﬁcients are given by [137] wˆMV DR = R−1 V V d dHR−1 V V d . (25) The MVDR beamformer is not robust against an inaccurate”