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hidden-semi-markov-model
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Fault Detection and Smart Monitoring of Industrial Fans Based on Vibration Signals
, M.Sc. Thesis Sharif University of Technology ; Manzuri Shalmani, Mohammad Taghi (Supervisor)
Abstract
Data Oriented Smart Monitoring for Industrial Machineries include approaches for fault detection and prognosis which only rely on non-stationary signals sampled from sensors and do not rely on physical model of machineries nor expert knowledge. Fault detection is task of determining state of machinery in present moment using past data. But in Prognosis focus is on predicting future state of machinery using past data. Most researches in this category are based on supervised algorithms, but in many applications labeling data is expensive. In this thesis some approaches for semi-superviseddiagnosis, based on markov random walk an K-NN have been implemented, also some improvements for K-NN have...
Average voice modeling based on unbiased decision trees
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Mons ; Volume 7911 LNAI , June , 2013 , Pages 89-96 ; 03029743 (ISSN) ; 9783642388460 (ISBN) ; Khorram, S ; Sameti, H ; Sharif University of Technology
2013
Abstract
Speaker adaptive speech synthesis based on Hidden Semi-Markov Model (HSMM) has been demonstrated to be dramatically effective in the presence of confined amount of speech data. However, we could intensify this effectiveness by training the average voice model appropriately. Hence, this study presents a new method for training the average voice model. This method guarantees that data from every speaker contributes to all the leaves of decision tree. We considered this fact that small training data and highly diverse contexts of training speakers are considered as disadvantages which degrade the quality of average voice model impressively, and further influence the adapted model and synthetic...
Apnea bradycardia detection based on new coupled hidden semi Markov model
, Article Medical and Biological Engineering and Computing ; 12 November , 2020 ; Shamsollahi, M. B ; Ge, D ; Beuchee, A ; Hernandez, A. I ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2020
Abstract
In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the...
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
Apnea-bradycardia is a medical term for prolonged respiratory pause accompanied with a heart rate reduction which is a common event among preterm infants. Repetition of apnea-bradycardia episodescompromises oxygenation and tissue perfusion and may lead to neurological impairment or even short-term morbi-mortality. Main solution to this breathing-related disorder is continues monitoring of infants in neonatal intensive care units in order to detect apnea-bradycardia event, generate an alarm and warn available nurse or physician to initiate quick nursing actions. Various studies have been done in this area and different methods are proposed which mainly focus on cardiac signal processing. This...
Markov Decision Process with Timeconsuming Transition
, M.Sc. Thesis Sharif University of Technology ; Alishahi, Kasra (Supervisor)
Abstract
Mankind according to his authority (or delusion of authority) always finds himself in a situation which need decision-¬making. Usually, he seeks to make the best possible decision. The basis for measuring the goodness of choices is different in different occasions. This measure could be level of enjoyment, economic profit, probability of reaching a goal, etc. These decisions have consequences such that the situations before and after the decisions are not the same. Most challenging decision¬-making situations are those which the decision¬maker has not the complete authority over the situation and the results of decisions are influenced by out of control factors. A significant part of...
Early detection of apnea-bradycardia episodes in preterm infants based on coupled hidden Markov model
, Article IEEE International Symposium on Signal Processing and Information Technology, IEEE ISSPIT 2013 ; 2013 , Pages 243-248 ; Montazeri, N ; Shamsollahi, M. B ; Ge, D ; Beuchee, A ; Pladys, P ; Hernandez, A. I ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
The incidence of apnea-bradycardia episodes in preterm infants may lead to neurological disorders. Prediction and detection of these episodes are an important task in healthcare systems. In this paper, a coupled hidden Markov model (CHMM) based method is applied to detect apnea-bradycardia episodes. This model is evaluated and compared with two other methods based on hidden Markov model (HMM) and hidden semi-Markov model (HSMM). Evaluation and comparison are performed on a dataset of 233 apnea-bradycardia episodes which have been manually annotated. Observations are composed of RR-interval time series and QRS duration time series. The performance of each method was evaluated in terms of...
Context-dependent acoustic modeling based on hidden maximum entropy model for statistical parametric speech synthesis
, Article Eurasip Journal on Audio, Speech, and Music Processing ; Vol. 2014, Issue. 1 , 2014 ; ISSN: 1687-4714 ; Sameti, H ; Bahmaninezhad, F ; King, S ; Drugman, T ; Sharif University of Technology
Abstract
Decision tree-clustered context-dependent hidden semi-Markov models (HSMMs) are typically used in statistical parametric speech synthesis to represent probability densities of acoustic features given contextual factors. This paper addresses three major limitations of this decision tree-based structure: (i) The decision tree structure lacks adequate context generalization. (ii) It is unable to express complex context dependencies. (iii) Parameters generated from this structure represent sudden transitions between adjacent states. In order to alleviate the above limitations, many former papers applied multiple decision trees with an additive assumption over those trees. Similarly, the current...
Improving Speech Signal Models for Statistical Parametric Speech Synthesis
, Ph.D. Dissertation Sharif University of Technology ; Sameti, Hossein (Supervisor)
Abstract
Statistical parametric speech synthesis (SPSS) has dominated speech synthesis research area over the last decade, due to its remarkable advantages such as high intelligibility and flexibility. Decision tree-clustered context-dependent hidden semi-Markov models are typically used in SPSS to represent probability densities of acoustic features given contextual factors. This research addresses four major limitations of this decision tree-based structure: (a) The decision tree structure lacks adequate context generalization; (b) It is unable to express complex context dependencies; (c) Parameters generated from this structure represent sudden transitions between adjacent states; (e) This...