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A time domain algorithm for blind separation of convolutive sound mixtures and \(L_1\) constrained minimization of cross correlations. (English) Zbl 1173.94373

Summary: A time domain blind source separation algorithm of convolutive sound mixtures is studied based on a compact partial inversion formula in closed form. An \(l_1\)-constrained minimization problem is formulated to find demixing filter coefficients for source separation while capturing scaling invariance and sparseness of solutions. The minimization aims to reduce (lagged) cross correlations of the mixture signals, which are modeled stochastically. The problem is non-convex, however it is put in a nonlinear least squares form where the robust and convergent Levenberg-Marquardt iterative method is applicable to compute local minimizers. Efficiency is achieved in recovering lower dimensional demixing filter solutions than the physical ones. Computations on recorded and synthetic mixtures show satisfactory performance, and are compared with other iterative methods.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)
65H10 Numerical computation of solutions to systems of equations
65C60 Computational problems in statistics (MSC2010)
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