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Design of a Non-negative Neural Network to Improve on NMF
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-8534-7622
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
2020 (English)In: 28thEuropean Signal Processing Conference (EUSIPCO 2020), Institute of Electrical and Electronics Engineers (IEEE) , 2020, p. 461-465Conference paper, Published paper (Refereed)
Abstract [en]

For prediction of a non-negative target signal using a non-negative input, we design a feed-forward neural network to achieve a better performance than a non-negative matrix factorization (NMF) algorithm. We provide a mathematical relation between the neural network and NMF. The architecture of the neural network is built on a property of rectified-linearunit (ReLU) activation function and a convex optimization layerwise training approach. For an illustrative example, we choose a speech enhancement application where a clean speech spectrum is estimated from a noisy spectrum.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2020. p. 461-465
Series
European Signal Processing Conference, ISSN 2076-1465
Keywords [en]
Neural networks, non-negative matrix factorization, speech enhancement
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-295263DOI: 10.23919/Eusipco47968.2020.9287668ISI: 000632622300093Scopus ID: 2-s2.0-85099314228OAI: oai:DiVA.org:kth-295263DiVA, id: diva2:1560153
Conference
28th European Signal Processing Conference (EUSIPCO), JAN 18-22, 2021, ELECTR NETWORK
Note

QC 20210621

Available from: 2021-06-03 Created: 2021-06-03 Last updated: 2023-04-05Bibliographically approved

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Tsai, FilipJavid, Alireza M.Chatterjee, Saikat

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • 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