Projects per year
Abstract
Singlechannel source separation problems occur when a number of sources emit
signals that are mixed and recorded by a single sensor, and we are interested
in estimating the original source signals based on the recorded mixture. This
problem, which occurs in many sciences, is inherently underdetermined and its
solution relies on making appropriate assumptions concerning the sources.
This dissertation is concerned with modelbased probabilistic singlechannel
source separation based on nonnegative matrix factorization, and consists of
two parts: i) three introductory chapters and ii) five published papers. The
first part introduces the singlechannel source separation problem as well as
nonnegative matrix factorization and provides a comprehensive review of existing
approaches, applications, and practical algorithms. This serves to provide
context for the second part, the published papers, in which a number of methods
for singlechannel source separation based on nonnegative matrix factorization
are presented. In the papers, the methods are applied to separating audio signals
such as speech and musical instruments and separating different types of
tissue in chemical shift imaging.
Original language  English 

Place of Publication  Kgs. Lyngby 

Publisher  Technical University of Denmark, DTU Informatics, Building 321 
Publication status  Published  Jan 2009 
Series  IMMPHD2008205 

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Projects
 1 Finished

Signal separation using independent component analysis with explicit source modelling
Schmidt, M. N., Larsen, J., Hansen, L. K. & Jutten, C.
01/02/2005 → 28/01/2009
Project: PhD