Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Gamma Hidden Markov Model as a Probabilistic Nonnegative Matrix Factorization
KTH, School of Electrical Engineering (EES), Communication Theory.
KTH, School of Electrical Engineering (EES), Communication Theory.
KTH, School of Electrical Engineering (EES), Communication Theory.
2013 (English)In: 2013 Proceedings of the 21st European Signal Processing Conference (EUSIPCO), European Signal Processing Conference , 2013, 6811626- p.Conference paper, Published paper (Refereed)
Abstract [en]

Among different Nonnegative Matrix Factorization (NMF) approaches, probabilistic NMFs are particularly valuable when dealing with stochastic signals, like speech. In the current literature, little attention has been paid to develop NMF methods that take advantage of the temporal dependencies of data. In this paper, we develop a hidden Markov model (HMM) with a gamma distribution as output density function. Then, we reformulate the gamma HMM as a probabilistic NMF. This shows the analogy of the proposed HMM and NMF, and will lead to a new probabilistic NMF approach in which the temporal dependencies are also captured inherently by the model. Furthermore, we propose an expectation maximization (EM) algorithm to estimate all the model parameters. Compared to the available probabilistic NMFs that model data with Poisson, multinomial, or exponential distributions, the proposed NMF is more suitable to be used with continuous-valued data. Our experiments using speech signals shows that the proposed approach leads to a better compromise between sparsity, goodness of fit, and temporal modeling compared to state-of-the-art.

Place, publisher, year, edition, pages
European Signal Processing Conference , 2013. 6811626- p.
Keyword [en]
Hidden Markov Model (HMM), Nonnegative Matrix Factorization (NMF), Expectation Maximization (EM) algorithm
National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-124355ISI: 000341754500239Scopus ID: 2-s2.0-84901293275ISBN: 978-099286260-2 (print)OAI: oai:DiVA.org:kth-124355DiVA: diva2:634174
Conference
2013 21st European Signal Processing Conference, EUSIPCO 2013; Marrakech; Morocco; 9 September 2013 through 13 September 2013
Note

QC 20130703

Available from: 2013-06-28 Created: 2013-06-28 Last updated: 2014-10-20Bibliographically approved

Open Access in DiVA

fulltext(636 kB)537 downloads
File information
File name FULLTEXT01.pdfFile size 636 kBChecksum SHA-512
334156bb9f830def42e27dcd99bc1c91215e660089875876c4d35ec5e5c2209181bdd14ff711e0fa7ebb90ca592d858a2da27fdfacf4941b1d8ab93e7c82b35f
Type fulltextMimetype application/pdf

Scopus

Search in DiVA

By author/editor
Mohammadiha, NasserKleijn, W. BastiaanLeijon, Arne
By organisation
Communication Theory
Engineering and TechnologyElectrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 537 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 476 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf