Hidden Markov models, theory and applications

326 Pages·2011·8.145 MB·English

by  Dymarski P. (ed.)

HIDDEN MARKOV MODELS,
THEORY AND APPLICATIONS
Edited by Przemyslaw Dymarski
Hidden Markov Models, Theory and Applications
Edited by Przemyslaw Dymarski
Published by InTech
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Hidden Markov Models, Theory and Applications, Edited by Przemyslaw Dymarski
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Contents
Preface IX
Part 1 Tutorials and Theoretical Issues 1
Chapter 1 History and Theoretical Basics of Hidden Markov Models 3
Guy Leonard Kouemou
Chapter 2 Hidden Markov Models in Dynamic System
Modelling and Diagnosis 27
Tarik Al-ani
Chapter 3 Theory of Segmentation 51
Jüri Lember, Kristi Kuljus and Alexey Koloydenko
Chapter 4 Classification of Hidden Markov Models:
Obtaining Bounds on the Probability of Error
and Dealing with Possibly Corrupted Observations 85
Eleftheria Athanasopoulou and Christoforos N. Hadjicostis
Part 2 Hidden Markov Models in Speech
and Time-domain Signals Processing 111
Chapter 5 Hierarchical Command Recognition Based
on Large Margin Hidden Markov Models 113
Przemyslaw Dymarski
Chapter 6 Modeling of Speech Parameter Sequence Considering
Global Variance for HMM-Based Speech Synthesis 131
Tomoki Toda
Chapter 7 Using Hidden Markov Models
for ECG Characterisation 151
Krimi Samar, Ouni Kaïs and Ellouze Noureddine
Chapter 8 Hidden Markov Models in the Neurosciences 169
Blaettler Florian, Kollmorgen Sepp,
Herbst Joshua and Hahnloser Richard
VI Contents
Chapter 9 Volcano-Seismic Signal Detection and Classification
Processing Using Hidden Markov Models - Application
to San Cristóbal and Telica Volcanoes, Nicaragua 187
Gutiérrez, Ligdamis, Ramírez, Javier,
Ibañez, Jesús and Benítez, Carmen
Chapter 10 A Non-Homogeneous Hidden Markov Model
for the Analysis of Multi-Pollutant Exceedances Data 207
Francesco Lagona, Antonello Maruotti and Marco Picone
Part 3 Hidden Markov Models in Image
and Spatial Structures Analysis 223
Chapter 11 Continuous Hidden Markov Models
for Depth Map-Based Human Activity Recognition 225
Zia Uddin and Tae-Seong Kim
Chapter 12 Applications of Hidden Markov Models
in Microarray Gene Expression Data 249
Huimin Geng, Xutao Deng and Hesham H Ali
Chapter 13 Application of HMM to the Study
of Three-Dimensional Protein Structure 269
Christelle Reynès, Leslie Regad, Stéphanie Pérot,
Grégory Nuel and Anne-Claude Camproux
Chapter 14 Control Theoretic Approach
to Platform Optimization using HMM 291
Rahul Khanna, Huaping Liu and Mariette Awad

Preface
Hidden Markov Models (HMMs), although known for decades, have made a big ca-
reer nowadays and are still in state of development. This book presents theoretical is-
sues and a variety of HMMs applications. Each of the 14 chapters addresses theoretical
problems and refers to some applications, but the more theoretical parts are presented
in Part 1 and the application oriented chapters are grouped in Part 2 and 3.
Chapter 1 has an introductory character: the basic concepts (e.g. Maximum Likeli-
hood, Maximum a Posteriori and Maximum Mutual Information approaches to the
HMM training) are explained. Problems of discriminative training are also discussed
in1 (2) and (5) – in particular the Large Margin approach. Chapter (3) discusses the
united approach to the HMM segmentation (decoding) problem based on statistical
learning. The Viterbi training is compared with the Baum-Welch training in (2). The
HMM evaluation problem is analyzed in (4), where the probability of classifi cation er-
ror in presence of corrupted observations (e.g. caused by sensor failures) is estimated.
Chapter (6) presents the Global Variance constrained trajectory training algorithm for
the HMMs used for speech signal generation. The Hidden Semi-Markov Models and
Hidden Markov Trees are described in (7), the Pair HMMs in (8) and the Non-homo-
geneous HMMs in (10). The association of HMMs with other techniques, e.g. wavelet
transforms (7) has proved useful for some applications.
The HMMs applications concerning recognition, classifi cation and alignment of sig-
nals described in time domain are presented in Part 2. In (5) the hierarchical recog-
nition of spoken commands and in (6) the HMMs application in the Text-To-Speech
synthesis is described. Chapter (7) presents the algorithms of the ECG signal analysis
and segmentation. In (8) HMM applications in neurosciences are discussed, i.e. the
brain activity modeling, the separation of signals generated by single neurons given
a muti-neuron recording and the identifi cation and alignment of birdsong. In (9) the
classifi cation of seismic signals is described and in (10) multi-pollutant exceedances
data are analyzed.
The applications referring to images, spatial structures and other data are presented in
Part 3. Moving pictures (in forms of depth silhouett es) are recognized in the Human Ac-
tivity Recognition System described in (11). Some applications concern computational
1 Numbers of chapters are referred in parentheses
X Preface
biology, bioinformatics and medicine. Predictions of gene functions and genetic abnor-
malities are discussed in (12), a 3-dimensional protein structure analyzer is described
in (13) and a diagnosis of the sleep apnea syndrome is presented in (2). There are also
applications in engineering: design of the energy effi cient systems (e.g. server plat-
forms) is described in (14) and condition-based maintenance of machines – in (2).
I hope that the reader will fi nd this book useful and helpful for their own research.
Przemyslaw Dymarski
Warsaw University of Technology,Department of
Electronics and Information Technology,
Institute of Telecommunications
Poland