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MODEL ORDER SELECTION FOR NON-NEGATIVE MATRIX FACTORIZATIONWITH APPLICATION TO SPEECH ENHANCEMENT
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2011 (English)Report (Other academic)
Abstract [en]

This report deals with the application of non-negative matrixfactorization (NMF) in speech processing. A Bayesian NMFis used to find the optimal number of basis vectors for thespeech signal. The result is validated by performing a speechenhancement task for a set of different number of basis vec-tors. The algorithm performance is measured with the Sourceto Distortion Ratio (SDR) that represents the overall qualityof speech. The results show that for medium input SNRs,60 basis vectors for each speaker are sufficient to model thespeech spectrogram. NMF produced better SDR results thana recently developed version of Spectral Subtraction algo-rithm. The window length was found to have a great effecton the results, but zero padding did not influence the results.

Place, publisher, year, edition, pages
2011. , 5 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-42588OAI: oai:DiVA.org:kth-42588DiVA: diva2:447310
Funder
EU, European Research Council, 2008-214699
Note
QC 20111013Available from: 2011-10-13 Created: 2011-10-11 Last updated: 2011-10-13Bibliographically approved

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MOS Using BNMF(73 kB)600 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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Output format
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