This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM ...

introduction: evoked potentials arisen by stimulating the brain can be utilized as a communication tool  between humans and machines. most brain-computer interface (bci) systems use the p300 component,  which is an evoked potential. in this paper, we evaluate the use of the hidden markov model (hmm) for  detection of p300.  materials and methods: the wavelet transforms, wavelet-enhanced indepen...

Introduction: Evoked potentials arisen by stimulating the brain can be utilized as a communication tool  between humans and machines. Most brain-computer interface (BCI) systems use the P300 component,  which is an evoked potential. In this paper, we evaluate the use of the hidden Markov model (HMM) for  detection of P300.  Materials and Methods: The wavelet transforms, wavelet-enhanced indepen...

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long divided into several sections and the occurs in different section that defined as state of (HMM). The hybrid HMM, i.e., DNN-HMM, consists (DNN) with multiple layers exploit non-linear data. DNN initialized by using belief (DBN). DBN pre-trained built stacking top-down restr...

The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has been used widely as a natural Bayesian nonparametric extension of the classical for learning from sequential and time-series data. A sticky HDP-HMM proposed to strengthen self-persistence probability in HDP-HMM. However, entangles strength prior transition together, limiting its expressiveness. Here, we propose more general mo...

Abstract This work attempts to approximate a linear Gaussian system with finite-state hidden Markov model (HMM), which is found useful in dealing challenges designing networked control systems An indirect approach developed, where state-space (SSM) firstly identified for and the SSM then used as an emulator learning HMM. In proposed method, training data HMM are obtained from generated by throu...