Stuctured non-negative matrix factorization with sparsity patterns

Hans Laurberg, Mikkel N. Schmidt, Mads Græsbøll Christensen, Søren Holdt Jensen

AbstractIn this paper, we propose a novel algorithmformonaural blind source
separation based on non-negativematrix factorization (NMF). A shortcoming
of most source separation methods is the need for training
data for each individual source. The algorithm proposed in this paper
is able separate sources even when there is no training data for
the individual sources. The algorithm makes use of models trained
on mixed signals and uses training data where more than one source
is active at the time. This makes the algorithm applicable to situations
where recordings of the individual sources are unavailable. The
key idea is to construct a structure matrix that indicates where each
source is active, and we prove that this structure matrix, combined
with a uniqueness assumption, is sufficient to ensure that results are
equivalent to training on isolated sources. Our theoretical findings is
backed up by simulations on music data that show that the proposed
algorithm trained on mixed recordings performs as well as existing
NMF source separation methods trained on solo recordings.
TypeConference paper [With referee]
ConferenceSignals, Systems and Computers, Asilomar Conference on
Year2008    Month October
PublisherInformatics and Mathematical Modelling, Technical University of Denmark, DTU
AddressRichard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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