Structured 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 | Asilomar Conference on Signals, Systems and Computers |
| 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 |