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The signal subspace approach for speech enhancement is extended to colored-noise processes. Explicit forms for the linear time-domain- and spectral-domain-constrained estimators are presented. These estimators minimize the average signal distortion power for given constraints on the residual noise power in the time and spectral domains, respectively.(More)
We present a computationally-efficient matrix-vector expression for the solution of a matrix linear least squares problem that arises in multistatic antenna array processing. Our derivation relies on an explicit new relation between Kronecker, Khatri-Rao and Schur-Hadamard matrix products, which involves a selection matrix (i.e., a subset of the columns of(More)
A signal-subspace method is derived for the localization and imaging of unknown scatterers using intensity-only wave field data (lacking field phase information). The method is an extension of the time-reversal multiple-signal-classification imaging approach to intensity-only data. Of importance, the derived methodology works within exact scattering theory(More)
We present a brief overview of the speech enhancement problem for wide-band noise sources that are not correlated with the speech signal. Our main focus is on the spectral subtraction approach and some of its derivatives in the forms of linear and non-linear minimum mean square error estimators. For the linear case, we review the signal subspace approach,(More)