MODEL ORDER SELECTION FOR NON-NEGATIVE MATRIX FACTORIZATIONWITH APPLICATION TO SPEECH ENHANCEMENT
2011 (English)Report (Other academic)
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.
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-42588OAI: oai:DiVA.org:kth-42588DiVA: diva2:447310
FunderEU, European Research Council, 2008-214699
QC 201110132011-10-132011-10-112011-10-13Bibliographically approved