| Autor: |
Soren Vang Andersen, Karsten Vandborg Sørensen |
| Rok vydání: |
2007 |
| Předmět: |
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| Zdroj: |
Sørensen, K V & Andersen, S V 2007, ' Rayleigh Mixture Model-Based Hidden Markov Modeling and Estimation of Noise in Noisy Speech Signals ', IEEE Transactions on Audio Speech and Language Processing, vol. 15, no. 3, pp. 901-917 . https://doi.org/10.1109/TASL.2006.885240 |
| ISSN: |
1558-7916 |
| DOI: |
10.1109/tasl.2006.885240 |
| Popis: |
In this paper, we propose a new statistical model for noise periodogram modeling and estimation. The proposed model is a hidden Markov model (HMM) with a Rayleigh mixture model (RMM) in each state. For this new model, we derive an expectation-maximization (EM) training algorithm and a minimum mean-square error (MMSE) noise periodogram estimator. It is shown that when compared to the Gaussian mixture model (GMM)-based HMM, the RMM-based HMM has less computationally complex EM iterations and gives a better fit of the noise periodograms when the mixture models has a low number of components. Furthermore, we propose a specialization of the proposed model, which is shown to provide better MMSE noise periodogram estimates than any other of the tested HMM initializations for cyclo-stationary noise types |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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