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We propose a novel solution to the blind speech separation problem where the de-mixing transform is estimated only within selected frequency bins. This solution is based on Independent Vector Analysis applied to a subset of instantaneous mixtures, one per selected frequency bin. Next, two approaches are proposed to complete the transform: one based on null beamforming, and the other based on convex...
In this paper, we propose a low-rank tensor deconvolution problem which seeks multiway replicative patterns and corresponding activating tensors of rank-1. An alternating least squares (ALS) algorithm has been derived for the model to sequentially update loading components and the patterns. In addition, together with a good initialisation method using tensor diagonalization, the update rules have...
This report proposes a novel variant of the generalized sidelobe canceler. It assumes that a set of prepared relative transfer functions (RTFs) is available for several potential positions of a target source within a confined area. The key problem here is to select the correct RTF at any time, even when the exact position of the target is unknown and interfering sources are present. We propose to...
In this paper, a novel algorithm to blindly separate an instantaneous linear underdetermined mixture of nonstationary sources is proposed. It means that the number of sources exceeds the number of channels of the available data. The separation is based on the working assumption that the sources are piecewise stationary with a different variance in each block. It proceeds in two steps: 1) estimating...
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