scholarly journals Single-channel talker localization based on separation of the acoustic transfer function using hidden Markov model and its classification

2013 ◽  
Vol 34 (3) ◽  
pp. 176-186
Author(s):  
Ryoichi Takashima ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Transfer Function ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Single Channel ◽  
Hidden Markov ◽  
Acoustic Transfer Function ◽  
Talker Localization

scholarly journals An automatic single-channel EEG-based sleep stage scoring method based on hidden Markov Model

2019 ◽  
Vol 324 ◽  
pp. 108320 ◽  
Author(s):  
Hojat Ghimatgar ◽  
Kamran Kazemi ◽  
Mohammad Sadegh Helfroush ◽  
Ardalan Aarabi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Single Channel ◽  
Hidden Markov ◽  
Sleep Stage ◽  
Scoring Method ◽  
Sleep Stage Scoring

scholarly journals An automatic sleep staging method based on CRNN-HMM model

2021 ◽  
Vol 2078 (1) ◽  
pp. 012054
Author(s):  
Mengran Wu ◽  
Hong Xie ◽  
Huiping Shi
Keyword(s):  
Sleep Quality ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Short Term Memory ◽  
Single Channel ◽  
Hidden Markov ◽  
Classification Performance ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Data Set

Abstract Sleep staging is an important process for detecting sleep quality and diagnosing sleep disorders. However, traditional sleep staging is a labor-intensive task, and it is prone to subjective errors. Therefore, this paper innovatively proposes an automatic sleep staging model based on single-channel EOG—CRNN-HMM. The CRNN-HMM in this paper combines Convolutional recursive neural networks(CRNN) and hidden Markov model(HMM). The main idea of this model is to use CRNN to automatically extract features from EOG, and send the feature signals to a variant of RNN, Bi-directional Long Short-Term Memory(BiLSTM), to mine the dependencies between sleep stages and realize automatic staging of sleep data. Finally, a Hidden Markov Model is used to convert the prior information of the sleep phase of the adjacent EOG cycle in order to improve the classification performance of S1, thereby improving the classification performance of CRNN. The simulation results show that the overall accuracy of the model on the CAP-Sleep data set reaches 95.0%, which proves that the model can provide a way for the evaluation of sleep quality.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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