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GMM based Bayesian approach to speech enhancement in signal/transform domain
Indian Institute of Science.
Indian Institute of Science.ORCID iD: 0000-0003-2638-6047
Indian Institute of Science.
2008 (English)In: 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, New York: IEEE , 2008, 4893-4896 p.Conference paper, Published paper (Refereed)
Abstract [en]

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

Place, publisher, year, edition, pages
New York: IEEE , 2008. 4893-4896 p.
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keyword [en]
Speech enhancement, estimation
National Category
Signal Processing
Research subject
SRA - ICT
Identifiers
URN: urn:nbn:se:kth:diva-46574DOI: 10.1109/ICASSP.2008.4518754ISI: 000257456703207ISBN: 978-1-4244-1483-3 (print)OAI: oai:DiVA.org:kth-46574DiVA: diva2:453899
Conference
33rd IEEE International Conference on Acoustics, Speech and Signal Processing. Las Vegas, NV. MAR 30-APR 04, 2008
Note
QC 20111107Available from: 2011-11-03 Created: 2011-11-03 Last updated: 2012-01-20Bibliographically approved

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Chatterjee, Saikat

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CiteExportLink to record
Permanent link

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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