Sparse Non-negative Matrix Factor 2-D Deconvolution |
Morten Mørup, Mikkel N. Schmidt
|
| Abstract | Se instead "Shift Invariant Sparse Coding for Image and Music Data"
We introduce the non-negative matrix factor 2-D deconvolution
(NMF2D) model, which decomposes a matrix into a 2-dimensional
convolution of two factor matrices. This model is an extension of
the non-negative matrix factor deconvolution (NMFD) recently
introduced by Smaragdis (2004). We derive and prove
the convergence of two algorithms for NMF2D based on minimizing the
squared error and the Kullback-Leibler divergence respectively.
Next, we introduce a sparse non-negative matrix factor 2-D
deconvolution model that gives easy interpretable decompositions
and devise two algorithms for computing this form of factorization. The
developed algorithms have been used for source separation
and music transcription. |
| Type | Technical report |
| Journal/Book/Conference | Technical Report |
| Year | 2006 |
| Publisher | Technical University of Denmark |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |