On noise gain estimation for HMM-based speech enhancement
2005 (English)In: 9th European Conference on Speech Communication and Technology, 2005, 2113-2116 p.Conference paper (Refereed)
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.
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
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-36325ScopusID: 2-s2.0-33745221416OAI: oai:DiVA.org:kth-36325DiVA: diva2:430609
9th European Conference on Speech Communication and Technology; Lisbon
QC 201107112011-07-112011-07-112011-07-11Bibliographically approved