Aprendizado de Dicionários para a Separação Semi-Cega de Fontes por Matching Pursuit

Diego Barreto Haddad, Mariane Rembold Petraglia, Lisandro Lovisolo, Paulo Bulkool Batalheiro

DOI: 10.14209/sbrt.2013.96

Evento: XXXI Simpósio Brasileiro de Telecomunicações (SBrT2013)

Keywords: Source Separation Matching Pursuit Dictionary Learning

Abstract

The Sparse Component Analysis is one of the most used techniques for handle the source separation problem. In this paper, a technique based in atomic decompositions, is used in the identification of a mixture system. If isolated excerpts of the sources are available, we show that using adapted dicionaries can imply a better (with a inferior MSE in, approximately, 15 dB) and less computationally intensive identification procedure.

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