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On noise gain estimation for HMM-based speech enhancement
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
KTH, School of Electrical Engineering (EES), Sound and Image Processing.
2005 (English)In: 9th European Conference on Speech Communication and Technology, 2005, 2113-2116 p.Conference paper, Published paper (Refereed)
Abstract [en]

To address the variation of noise level in non-stationary noise signals, we study the noise gain estimation for speech enhancement using hidden Markov models (HMM). We consider the noise gain as a stochastic process and we approximate the probability density function (PDF) to be log-normal distributed. The PDF parameters are estimated for every signal block using the past noisy signal blocks. The approximated PDF is then used in a Bayesian speech estimator minimizing the Bayes risk for a novel cost function, that allows for an adjustable level of residual noise. As a more computationally efficient alternative, we also derive the maximum likelihood (ML) estimator, assuming the noise gain to be a deterministic parameter. The performance of the proposed gain-adaptive methods are evaluated and compared to two reference methods. The experimental results show significant improvement under noise conditions with time-varying noise energy.

Place, publisher, year, edition, pages
2005. 2113-2116 p.
Keyword [en]
Acoustic noise, Gain measurement, Markov processes, Maximum likelihood estimation, Probability density function, Random processes, Speech recognition, Bayesian speech estimator, Gain-adaptive methods, Hidden Markov models (HMM), Maximum likelihood (ML), Acoustic signal processing
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-36325Scopus ID: 2-s2.0-33745221416OAI: oai:DiVA.org:kth-36325DiVA: diva2:430609
Conference
9th European Conference on Speech Communication and Technology; Lisbon
Note
QC 20110711Available from: 2011-07-11 Created: 2011-07-11 Last updated: 2011-07-11Bibliographically approved

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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