fbeamform¶
Implements beamformers in the frequency domain.
Basic class for implementing steering vectors with monopole source transfer models |
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Beamforming using the basic delay-and-sum algorithm in the frequency domain. |
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Functional beamforming after Dougherty, 2014. |
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Beamforming using the Capon (Mininimum Variance) algorithm, see Capon, 1969. |
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Beamforming using eigenvalue and eigenvector techniques, see Sarradj et al., 2005. |
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Beamforming using the MUSIC algorithm, see Schmidt, 1986. |
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CLEAN deconvolution, see Hoegbom, 1974. |
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DAMAS deconvolution, see Brooks and Humphreys, 2006. |
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DAMAS deconvolution, see Brooks and Humphreys, 2006, for solving the system of equations, instead of the original Gauss-Seidel iterations, this class employs the NNLS or linear programming solvers from scipy.optimize or one of several optimization algorithms from the scikit-learn module. |
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Orthogonal beamforming, see Sarradj, 2010. |
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CLEAN-SC deconvolution, see Sijtsma, 2007. |
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Covariance Matrix Fitting, see Yardibi et al., 2008. |
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SODIX, see Funke, Ein Mikrofonarray-Verfahren zur Untersuchung der Schallabstrahlung von Turbofantriebwerken, 2017. |
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Beamforming GIB methods with different normalizations, |
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Base class for array methods without predefined grid |
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Orthogonal beamforming without predefined grid |
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The point spread function. |
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Calculates the sound pressure level from the squared sound pressure. |
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Integrates a sound pressure map over a given sector. |