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Blind source separation of more sources than mixtures using overcomplete representations

Lee, TW; Lewicki, MS; Girolami, M; Sejnowski, TJ; (1999) Blind source separation of more sources than mixtures using overcomplete representations. IEEE SIGNAL PROC LET , 6 (4) 87 - 90.

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Abstract

Empirical results were obtained for the blind source separation of more sources than mixtures using a recently proposed framework for learning overcomplete representations. This technique assumes a linear mixing model with additive noise and involves two steps: 1) learning an overcomplete representation for the observed data and 2) inferring sources given a sparse prior on the coefficients, We demonstrate that three speech signals can be separated with good fidelity given only two mixtures of the three signals. Similar results were obtained with mixtures of two speech signals and one music signal.

Type:Article
Title:Blind source separation of more sources than mixtures using overcomplete representations
Keywords:blind source separation, independent component analysis, overcomplete dictionary, overcomplete representation, speech signal separation
UCL classification:UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science

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