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Rayleigh Mixture Model-Based Hidden Markov Modeling and Estimation of Noise in Noisy Speech Signals

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2 Author(s)
Karsten Vandborg Sorensen ; Dept. of Commun. Technol., Aalborg Univ. ; Sren Vang Andersen

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

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Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:15 ,  Issue: 3 )