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.
Cite as: Zhao, D.Y., Kleijn, W.B. (2005) On noise gain estimation for HMM-based speech enhancement. Proc. Interspeech 2005, 2113-2116, doi: 10.21437/Interspeech.2005-688
@inproceedings{zhao05_interspeech,
author={David Y. Zhao and W. Bastiaan Kleijn},
title={{On noise gain estimation for HMM-based speech enhancement}},
year=2005,
booktitle={Proc. Interspeech 2005},
pages={2113--2116},
doi={10.21437/Interspeech.2005-688}
}