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Most of the existing underdetermined blind source separation (BSS) approaches assume that the source signals are strictly or partially sparse. This paper, however, presents a BSS method in underdetermined mixing situation for non-sparse signals based on the local mean decomposition (LMD) algorithm. The BSS method firstly introduces LMD into the BSS problem to rebuild a few extra mixing signals. Such...
An improved local mean decomposition method is presented to compute the data of GMA signals, aiming at the non-stationary and non-linear time-varying characteristics of the GMA signals. The spline interpolation function method is used to compute local mean functions and envelope functions instead of smooth moving average method, which increases the accuracy and convergence rate of the algorithm. The...
An objective of blind source separation (BSS) is to recover potential source signals from their mixtures without a prior knowledge of the mixing process. In this paper, a new underdetermined blind source separation (UDBSS) approach, based on the local mean decomposition (LMD) method and the AMUSE algorithm, is proposed. To make the UDBSS problem simpler, some extra observation signals are first constructed...
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