Stuctured non-negative matrix factorization with sparsity patterns |
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| Abstract | In 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. |
| Type | Conference paper [With referee] |
| Conference | Signals, Systems and Computers, Asilomar Conference on |
| Year | 2008 Month October |
| Publisher | Informatics and Mathematical Modelling, Technical University of Denmark, DTU |
| Address | Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |