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On noise gain estim...
On noise gain estimation for HMM-based speech enhancement
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- Zhao, David Yuheng (författare)
- KTH,Ljud- och bildbehandling
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- Kleijn, W. Bastiaan (författare)
- KTH,Ljud- och bildbehandling
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(creator_code:org_t)
- 2005
- 2005
- Engelska.
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Ingår i: 9th European Conference on Speech Communication and Technology. ; , s. 2113-2116
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- 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
- Information technology
- Informationsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)