Blind source separation in underdetermined model based on local mean decomposition and AMUSE algorithm

Abstract

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 using the LMD method. Thus the underdetermined blind source separation problem is transformed into an (over-)determined one. Subsequently, the well known AMUSE algorithm is applied to these new observations to estimate the source signals. The proposed method does not resort to the sparsity constraint which is included in most of the former researches. The theoretical analysis and simulation results illustrate the effectiveness of the proposed UDBSS method.