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This paper considers the blind separation of nonstationary sources in the underdetermined convolutive mixture case. We introduce, two methods based on the sparsity assumption of the sources in the time-frequency (TF) domain. The first one assumes that the sources are disjoint in the TF domain, i.e., there is at most one source signal present at a given point in the TF domain. In the second method,...
In this paper, we address the problem of separating unknown multi-component signals from their instantaneous mixtures. Using linear time-frequency (TF) representation of the mixtures along with vectors classification scheme provide us a simple and efficient technique to separate multicomponent signals. The proposed algorithm can handle monocomponent as well as multicomponent sources and its assumptions...
In this paper, we address the problem of separating N unknown sources using as many observed mixtures. The sources considered here are assumed to be of a non-stationary nature, i.e., their spectral contents are assumed to be time-varying. Using linear time-frequency (TF) representations of the mixtures along with a classification procedure based on vector clustering yield an effective way to separate...
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