Low-rank blind nonnegative matrix deconvolution

Anh Huy Phan, Petr Tichavský, Andrzej Cichocki, Zbyněk Koldovský

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

A novel blind deconvolution is proposed to seek for basis patterns and their location maps inside a nonnegative data matrix. Basis patterns can have different sizes, and shift in independent directions. Moreover, the location maps can be low-rank or rank-one matrices composed by two relatively small and tall matrices or by two vectors. A general framework to solve this problem together with algorithms are introduced. The experiments on music and texture decomposition will confirm performance of our method, and of the proposed algorithms.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages1893-1896
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period25/03/1230/03/12

Keywords

  • music decomposition
  • nonnegative matrix deconvolution/factorization
  • pattern extraction

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