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Transcript of 1 Tutorial on Neural Network Models for Speech and Image Processing B. Yegnanarayana Speech & Vision...
1
Tutorial on
Neural Network Models for Speech and Image Processing
B. YegnanarayanaSpeech & Vision Laboratory
Dept. of Computer Science & EngineeringIIT Madras, Chennai-600036
WCCI 2002, Honululu, Hawaii, USAMay 12, 2002
2
Need for New Models of Computing for Speech & Image
Tasks
• Speech & Image processing tasks
• Issues in dealing with these tasks by human beings
• Issues in dealing with the tasks by machine
• Need for new models of computing in dealing with natural signals
• Need for effective (relevant) computing
• Role of Artificial Neural Networks (ANN)
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3
Organization of the Tutorial
Part I Feature extraction and classification problems with speech and image data
Part II Basics of ANN
Part III ANN models for feature extraction and classification
Part IV Applications in speech and image processing
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4
PART I
Feature Extraction and Classification Problems in
Speech and Image
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Feature Extraction and Classification Problems in Speech and Image
• Distinction between natural and synthetic signals (unknown model vs known model generating the signal)
• Nature of speech and image data (non-repetitive data, but repetitive features)
• Need for feature extraction and classification
• Methods for feature extraction and models for classification
• Need for nonlinear approaches (methods and models)
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6
Speech vs Audio
• Audio (audible) signals (noise, music, speech and other signals)
• Categories of audio signals
– Audio signal vs non-signal (noise)
– Signal from speech production mechanism vs other audio signals
– Non-speech vs speech signals (like with natural language)
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Speech Production Mechanism
< Back
8
Different types of sounds
< Back
9
Categorization of sound units
< Back
10
Nature of Speech Signal
• Digital speech: Sequence of samples or numbers
• Waveform for word “MASK” (Figure)
• Characteristics of speech signal– Excitation source characteristics
– Vocal tract system characteristics
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Waveform for the word “mask”
<Back
12
Source-System Model of Speech Production
Impulsetrain generator
Randomnoise generator
Time-varyingdigital filterX
Voice/unvoiced
switch
G
u(n)s(n)
Pitch period
Vocal tract parameters
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13
Features from Speech Signal (demo)
• Different components of speech (speech, source and system)
• Different speech sound units (Alphabet in Indian Languages)
• Different emotions
• Different speakers
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Speech Signal Processing Methods
• To extract source-system features and suprasegmental features
• Production-based features
• DSP-based features
• Perception-based features
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Models for Matching and Classification
• Dynamic Time Warping (DTW)
• Hidden Markov Models (HMM)
• Gaussian Mixture Models (GMM)
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Applications of Speech Processing
• Speech recognition
• Speaker recognition/verification
• Speech enhancement
• Speech compression
• Audio indexing and retrieval
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17
Limitations of Feature Extraction Methods and Classification Models
• Fixed frame analysis
• Variability in the implicit pattern
• Not pattern-based analysis
• Temporal nature of the patterns
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Need for New Approaches
• To deal with ambiguity and variability in the data for feature extraction
• To combine evidence from multiple sources (classifiers and knowledge sources)
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Images• Digital Image - Matrix of numbers
• Types of Images
– line sketches, binary, gray level and color
– Still images, video, multimedia
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Image Analysis
• Feature extraction• Image segmentation: Gray level, color,
texture• Image classification
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Processing of Texture-like Images2-D Gabor Filter
A typical Gaussian filter with =30
A typical Gabor filter with
=30, =3.14 and =45
)sincos(2
1exp
2
1),,,,,( ))()(( 22
yxj
yxyxf
yxyxyx
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Limitations
• Feature extraction• Matching• Classification methods/models
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Need for New Approaches
• Feature extraction: PCA and nonlinear PCA
• Matching: Stereo images
• Smoothing: Using the knowledge of image and not noise
• Edge extraction and classification: Integration of global and local information or combining evidence
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PART II
Basics of ANN
25
• Problem solving: Pattern recognition tasks by human and machine
• Pattern vs data
• Pattern processing vs data processing
• Architectural mismatch
• Need for new models of computing
Artificial Neural Networks
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Biological Neural Networks
• Structure and function: Neurons, interconnections, dynamics for learning and recall
• Features: Robustness, fault tolerance, flexibility, ability to deal with variety of data situations, collective computation
• Comparison with computers: Speed, processing, size and complexity, fault tolerance, control mechanism
• Parallel and Distributed Processing (PDP) models
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Basics of ANN
• ANN terminology: Processing unit (fig), interconnection, operation and update (input, weights, activation value, output function, output value)
• Models of neurons: MP neuron, perceptron and adaline
• Topology (fig)• Basic learning laws (fig)
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Model of a Neuron
<back
29
Topology
<back
30
Basic Learning Laws
<back
31
Activation and Synaptic Dynamic Models
• General activation dynamics model
ij
ijjjiiiiiiiiiiiii wxfJxDExfIxCBxAtx ))()(())()(()(
Passive decay term
Excitatory term Inhibitory term
• Synaptic dynamics model
)()()()( tststwtw jiijij Passive
decay term
Correlation term
• Stability and convergence<Prev
Next>
32
Functional Units and Pattern Recognition Tasks
• Feedforward ANN– Pattern association– Pattern classification– Pattern mapping/classification
• Feedback ANN– Autoassociation– Pattern storage (LTM)– Pattern environment storage (LTM)
• Feedforward and Feedback (Competitive Learning) ANN– Pattern storage (STM)– Pattern clustering– Feature map
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33
Two Layer Feedforward Neural Network (FFNN)
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PR Tasks by FFNN• Pattern association
– Architecture: Two layers, linear processing, single set of weights– Learning:, Hebb's (orthogonal) rule, Delta (linearly independent) rule– Recall: Direct– Limitation: Linear independence, number of patterns restricted to input dimensionality– To overcome: Nonlinear processing units, leads to a pattern classification problem
• Pattern classification– Architecture: Two layers, nonlinear processing units, geometrical interpretation– Learning: Perceptron learning– Recall: Direct– Limitation: Linearly separable functions, cannot handle hard problems– To overcome: More layers, leads to a hard learning problem
• Pattern mapping/classification– Architecture: Multilayer (hidden), nonlinear processing units, geometrical interpretation– Learning: Generalized delta rule (backpropagation)– Recall: Direct– Limitation: Slow learning, does not guarantee convergence– To overcome: More complex architecture
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Perceptron Network
• Perceptron classification problem• Perceptron learning law• Perceptron convergence theorem• Perceptron representation problem• Multilayer perceptron
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Geometric Interpretation of Perceptron Learning
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Generalized Delta Rule (Backpropagation Learning)
ok
okk
ok
hj
okkj ffbsssw )(,
K
k
okkj
hj
hji
hj
hji swfsasw
1
,
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38
Issues in Backpropagation Learning
• Description and features of error backpropagation
• Performance of backpropagation learning• Refinements of backpropagation learning• Interpretation of results of learning• Generalization• Tasks with backpropagation network• Limitations of backpropagation learning• Extensions to backpropagation
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PR Tasks by FBNN• Autoassociation
– Architecture: Single layer with feedback, linear processing units– Learning: Hebb (orthogonal inputs), Delta (linearly independent inputs)– Recall: Activation dynamics until stable states are reached– Limitation: No accretive behavior– To overcome: Nonlinear processing units, leads to a pattern storage problem
• Pattern Storage– Architecture: Feedback neural network, nonlinear processing units, states, Hopfield
energy analysis– Learning: Not important– Recall: Activation dynamics until stable states are reached– Limitation: Hard problems, limited number of patterns, false minima– To overcome: Stochastic update, hidden units
• Pattern Environment Storage– Architecture: Boltzmann machine, nonlinear processing units, hidden units,
stochastic update– Learning: Boltzmann learning law, simulated annealing– Recall: Activation dynamics, simulated annealing– Limitation: Slow learning– To Overcome: Different architecture
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Hopfield Model• Model• Pattern storage condition
• Capacity of Hopfield model: Number of patterns for a given probability of error
kikjj
ij aaw )sgn(
lj
L
lliij aa
Nw
Lk
Ni
1
1
,...1
,...1where
• Energy analysis:
02
1
V
ssswV iijiij
Continuous Hopfield model: x
x
e
exf
1
1)(Next >< Prev
41
State Transition Diagram
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42
Computation of Weights for Pattern Storage
Patterns to be stored (111) and (010).
Results in set of inequalities to be satisfied.
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Pattern Storage Tasks• Hard problems : Conflicting requirements on a
set of inequalities• Hidden units: Problem of false minima• Stochastic update
Stochastic equilibrium: Boltzmann-Gibbs Law Z
esP
T
E
)(Next >< Prev
44
Simulated Annealing
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Boltzmann Machine
• Pattern environment storage• Architecture: Visible units, hidden
units, stochastic update, simulated annealing
• Boltzmann Learning Law:
)(
ijijpp
Twij
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Discussion on Boltzmann Learning• Expression for Boltzmann learning
– Significance of p+ij and p-
ij
– Learning and unlearning– Local property– Choice of and initial weights
• Implementation of Boltzmann learning– Algorithm for learning a pattern environment– Algorithm for recall of a pattern– Implementation of simulated annealing– Annealing schedule
• Pattern recognition tasks by Boltzmann machine– Pattern completion– Pattern association– Recall from noisy or partial input
• Interpretation of Boltzmann learning– Markov property of simulated annealing– Clamped-free energy and full energy
• Variations of Boltzmann learning– Deterministic Boltzmann machine– Mean-field approximation
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47
Competitive Learning Neural Network (CLNN)
Output layer with on-center and off-surroundconnections
Input layer
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PR Tasks by CLNN• Pattern storage (STM)
– Architecture: Two layers (input and competitive), linear processing units– Learning: No learning in FF stage, fixed weights in FB layer– Recall: Not relevant– Limitation: STM, no application, theoretical interest– To overcome: Nonlinear output function in FB stage, learning in FF stage
• Pattern clustering (grouping)– Architecture: Two layers (input and competitive), nonlinear processing units in
the competitive layer– Learning: Only in FF stage, Competitive learning– Recall: Direct in FF stage, activation dynamics until stable state is reached in
FB layer– Limitation: Fixed (rigid) grouping of patterns– To overcome: Train neighbourhood units in competition layer
• Feature map– Architecture: Self-organization network, two layers, nonlinear processing units,
excitatory neighbourhood units– Learning: Weights leading to the neighbourhood units in the competitive layer– Recall: Apply input, determine winner– Limitation: Only visual features, not quantitative– To overcome: More complex architecture
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Learning Algorithms for PCA networks
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Self Organization Network
(a) Network structure (b) Neighborhood regions at different times in the output layer
Input layer
Output layer
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Illustration of SOM
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PART III
ANN Models for Feature Extraction and Classification
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Neural Network Architecture and Models for Feature Extraction
• Multilayer Feedforward Neural Network (MLFFNN)
• Autoassociative Neural Networks (AANN)
• Constraint Satisfaction Models (CSM)• Self Organization MAP (SOM)• Time Delay Neural Networks (TDNN)• Hidden Markov Models (HMM)
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Multilayer FFNN
• Nonlinear feature extraction followed by linearly separable classification problem
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• Complex decision hypersurfaces for classification
• Asymptotic approximation of a posterior class probabilities
Multilayer FFNN
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• Radial Basis Function NN: Clustering followed by classification
Input vector a
Basis function
j
j(a)
Class labels
c1
cN
Radial Basis Function
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• Architecture• Nonlinear PCA• Feature extraction• Distribution capturing ability
Autoassociation Neural Network (AANN)
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Autoassociation Neural Network (AANN)
• Architecture
Input Layer Output LayerDimension Compression Hidden Layer
<Back
59
Distribution Capturing Ability of AANN
• Distribution of feature vector (fig) • Illustration of distribution in 2D
case (fig) • Comparison with Gaussian Mixture
Model (fig)
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Distribution of feature vector
<Back
61
(a) Illustration of distribution in 2D case(b,c) Comparison with Gaussian Mixture Model
<Back
62
Feature Extraction by AANN
• Input and output to AANN: Sequence of signal samples (captures dominant 2nd order statistical features)
• Input and output to AANN: Sequence of Residual samples (captures higher order statistical features in the sample sequence)
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63
Constraint Satisfaction Model
• Purpose: To satisfy the given (weak) constraints as much as possible
• Structure: Feedback network with units (hypotheses), connections (constraints / knowledge)
• Goodness of fit function: Depends on the output of unit and connection weights
• Relaxation Strategies: Deterministic and Stochastic
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Application of CS Models
• Combining evidence• Combining classifiers outputs• Solving optimization problems
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Self Organization Map (illustrations)
• Organization of 2D input to 1D feature mapping
• Organization of 16 Dimensional LPC vector to obtain phoneme map
• Organization of large document files
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Time Delay Neural Networks for Temporal Pattern Recognition
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Stochastic Models for Temporal Pattern Recognition
• Maximum likelihood formulation: Determine the class w, given the observation symbol sequence y, using criterion
• Markov Models
• Hidden Markov Models
)/(max wyPw
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68
PART IV
Applications in Speech & Image Processing
69
Applications in Speech and Image Processing
• Edge extraction in texture-like images
• Texture segmentation/classification by CS model
• Road detection from satellite images
• Speech recognition by CS model
• Speaker recognition by AANN model
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Problem of Edge Extraction in Texture-like Images
• Nature of texture-like images• Problem of edge extraction• Preprocessing (1-D) to derive partial evidence• Combining evidence using CS model
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• Texture Edges are the locations where there is an abrupt change in texture properties
Problem of Edge Extraction
Image with 4 natural texture regions
Edgemap showing micro edges
Edgemap showing macro edges
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1-D processing using Gabor Filter and Difference Operator
• 1-D Gabor smoothing filter : Magnitude and Phase
)2
exp(2
1),,(
2
2xj
xxf
1-D Gabor Filter: Gaussian modulated by a complex sinusoidal
Odd Component
Even Component
)cos()2
exp(2
1)(
2
2x
xxfc
)sin()2
exp(2
1)(
2
2x
xxfs
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• Differential operator for edge evidence – First derivative of 1-D Gaussian function
• Need for a set of Gabor filters
1-D processing using Gabor filter and Difference operator (contd.)
)2
exp(2
)(2
23 yy
yc
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74
Texture Edge Extraction using 1-D Gabor Magnitude and Phase
• Apply 1-D Gabor filter along each of the parallel lines of an image in one direction ( say, horizontal )
• Apply all Gabor filters of the filter bank in a similar way
• For each of the Gabor filtered output, partial edge information is extracted by applying the 1-D differential operator in the orthogonal direction ( say, vertical )
• The entire process is repeated in the orthogonal (vertical and horizontal) directions to obtain the partial edge evidence in the other direction
• The partial edge evidence is combined using a Constraint Satisfaction Neural Network Model
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Bank of 1-D Gabor Filters
Input Image
Filtered Image
Edge evidence
Combining the Edge evidence using Constraint Satisfaction Neural Network Mode
Edge map
Texture Edge Extraction using a set of 1-D Gabor Filters
Post-processing using 1-D Differential operator and Thresholding
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76
Structure of 3-D CSNN Model
I
J
K
3D lattice of size IxJxKConnections among the
nodes across the layers of for each pixel
+ve
-veConnections from a set of neighboring
nodes to each node in the same layer.
Combining Evidence using CSNN model
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77
Combining the Edge Evidence using Constraint Satisfaction Neural Network
(CSNN) Model
• Neural network model contains nodes arranged in a 3-D lattice structure
• Each node corresponds to a pixel in the post-processed Gabor filter output
• Post processed output of a single 1-D Gabor filter is an input to one 2-D layer of nodes
• Different layers of nodes, each corresponding to a particular filter output, are stacked one upon the other to form the 3-D structure
• Each node represents a hypothesis• Connection between two nodes represents a constraint
• Each node is connected to other nodes with inhibitory and excitatory connections
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78
31or 1 1 if , 8
1
21or 1 1 if , 16
1
11or 11 if , 8
1
,1,1,,,
jjii
jjii
jjii
W kjikji
)1(2
1 and 1,,,,,
K
W kjikji
Let, represents the weight of the connection from node (i,j,k) to node (i1,j1,k) within each layer k, and the weight represents the constraint between the nodes in two different layers (k and k1) in the same column. These are given as:
1,1,1,,, kjikjiW
1,1,1,,, kjikjiW
• The node is connected to other nodes in the same column with excitatory connections
Combining Evidence using CSNN model (contd.)
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79
• Using the notation as the output of the node (i,j,k), and the set as the state if the network
• The state of the neural network model is initialized using:
• In the deterministic relaxation method, the state of the network is updated iteratively by changing the output of each node at one time
• The state of each node is obtained using:
Ui,j,k (n) = Wi,j,k,i1,j1,k i1,j1,k + Wi,j,k,i,j,k1 i,j,k1 +Ii,j,k
Where Ui,j,k(n) is the net input to node(i,j,k) at nth iteration, and Ii,j,k is the external input given to the node (i,j,k)
• The state of the network is updated using:
where is the threshold
}1,0{,, kji
},,{ ,,, kjikji
otherwise ,0
pixel edge an of evidence has pixel theif ,1)0(,,, kji
otherwise
if Un i,j,k
kji ,0
,1)1(,,
Combining Evidence using CSNN model (contd.)
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Comparison of Edge Extraction using Gabor Magnitude and Gabor Phase
2-D Gabor Filter 1-D Gabor Magnitude
Texture Image 1-D Gabor Phase
2-D Gabor Filter1-D Gabor MagnitudeTexture Image
1-D Gabor Phase
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Texture Segmentation and Classification
• Image analysis (revisited)• Problem of texture segmentation and
classification • Preprocessing using 2D Gabor filter to derive
feature vector • Combining the partial evidence using CS model
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CS Model for Texture Classification
• Supervised and unsupervised problem• Modeling of image constraint• Formulation of a posterior probability CS
model• Hopfield neural network model and its energy
function • Deterministic and Stochastic relaxation
strategies
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83
CS Model for Texture Classification- Modeling of Image Constraints
• Feature formation process: Defined by the conditional probability of the feature vector gs of each pixels given the model
parameter of each class k.
2
2/||||
)2()|(
22
kM
g
sss
kksekLgGP
• Partition process: Defines the probability of the label of a pixel given the label of the pixels in its pth order neighborhood.
p
LL
psrrs Z
eNLLP
psNr
rs
)(
),|(
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• Label competition process: Describes the conditional probability of assigning a new label to an already labeled pixel
c
lk
lss Z
elLkLP
l
)(
)|(
84
CS Model for Texture Classification- Modeling of Image Constraints (contd.)
• Formulation of Posteriori Probability
lspsrsss lLNLgGkLE
lspsrsss
eZ
lLNrLgGkLP
),,,|(1
),,,|(
where
lNrrsk
m
k
ks
lspsrsss
lkLLg
lLNrLgGkLE
ps
)()())2ln((2
1
2
||||
),,,|(
22
2
and
lspsrs
ksss
total lLNrLgGkLEE ),,,|(,
)()( kLPgGPZZZ ssscp
• Total energy of the system
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85
Connections among the nodes across the layers
of for each pixel
+ve-ve
CS Model for Texture Classification
Connections from a set of neighboring
nodes to each node in the same layer.
E
state
(ij1)
I
J
K
k(ijk)
(ijK)
(ij1)
(ijk)
(ijK)
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86
Hopfield Neural Network and its Energy Function
ii
iiii i
iiHopfield OBOOWE 1
11,2
1
kjikji
kjikjikjikji kji
kjikjiHopfield OBOOWE ,,
,,,,1,1,1,,
,, 1,1,11,1,1,,,2
1
o1 oj oN
B1 Bj BN
IJ
K
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87
Natural Textures Initial Classification Final Classification
< Back
Results of Texture Classification - Natural Textures
88
Band-2 IRS image containing 4 texture
classes Initial Classification Final Classification
< Back
Results of Texture Classification - Remotely Sensing Data
89
SIR-C/X-SAR image of Lost City of Ubar
Classification using multispectral information
Classification using multispectral and textural
information
< Back
Results of Texture Classification - Multispectral Data
90
Speech Recognition using CS Model
• Problem of recognition of SCV unit (Table)• Issues in classification of SCVs(Table)• Representation of isolated utterance of
SCV unit– 60ms before and 140 ms after vowel
onset point– 240 dimensional feature vector
consisting of weighted cepstral coefficients
• Block diagram of the recognition system for SCV unit (Fig)
• CS network for classification of SCV unit(Fig)
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91
Problem of Recognition of SCV Units
<Back
92
Issues in Classification of SCVs
• Importance of SCVs – High frequency of occurrence: About 45%
• Main Issues in Classification of SCVs– Large number of SCV classes – Similarity among several SCVs classes
• Model of Classification of SCVs– Should have good discriminatory capablity( Artificial neural networks )- Should be able to handle large number of classes( Neural networks based on a modular approach )
<Back
93
Block Diagram of Recognition
System for SCV Units
<Back
94
CS Network for Classification of SCV Units
External evidence of bias for the node is computed using the
output of the MLFFNN5
External evidence of bias for the node is computed using the
output of the MLFFNN1
External evidence of bias for the node is
computed using the output of the MLFFNN9
Vowel Feedback Subnetwork
MOA Feedback Subnetwork
POA Feedback Subnetwork
<Back
95
Classification Performance of CSM and other SCV Recognition Systems
on Test Data of 80 SCV Classes
Decision CriteriaSCV RecognitionSystem Case 1 Case 2 Case 3 Case 4HMM based system 45.5 59.2 65.9 71.480-class MLFFNN 45.3 59.7 66.9 72.2MOA modularnetwork
29.2 50.2 59.0 65.3
POA modularnetwork
35.1 56.9 69.5 76.6
Vowel modularnetwork
30.1 47.5 58.8 63.6
Combined evidencebased system
51.6 63.5 70.7 74.5
ConstraintSatisfaction model
65.6 75.0 80.2 82.6
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96
Speaker Verification using AANN Models and Vocal Tract System
Features• One AANN for each speaker • Verification by identification • AANN structure: 19L 38N 4N 38N 19 L• Feature: 19 weighted LPCC from 16th order
LPC for each frame of 27.5 ms and frame shift 13.75ms
• Training: Pattern mode, 100 epochs, 1 min of data
• Testing: Model giving highest confidence for 10 sec of test data
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97
Speaker Recognition using Source Features
• One model for each speaker • Structure of AANN: 40L 48N 12N 48N 40L• Feature: About 10 sec of data, 60 epochs• Testing: Select model giving highest
confidence for 2 sec of test data
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Other Applications
• Speech enhancement• Speech compression • Image compression• Character recognition• Stereo image matching
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99
Summary and Conclusions
• Speech and image processing: Natural tasks• Significance of pattern processing • Limitation of conventional computer architecture• Need for new models or architectures for pattern
processing tasks• Basics of ANN• Architecture of ANN for feature extraction and
classification • Potential of ANN for speech and image
processing
< Prev
100
References
1. B.Yegnanarayana, “ Artificial Neural Networks”, Prentice-Hall of India, New Delhi, 1999
2. L. R. Rabiner and B. H. Juang, “Fundamentals of Speech Recognition”, Prentice-Hall, New Jersey, 1993
3. Alan C. Bovik, Handbook of Image and Video Processing, Academic Press, 2001
4. Xuedong Hwang, Alex Acero and Hsiao-Wuen Hon, “Spoken Language Processing”, Prentice-Hall, New Jersey, 2001
5. P. P. Raghu, “Artificial Neural Network Models for Texture Analysis”, PhD Thesis, CSE Dept., IIT Madras, 1995
6. C. Chandra Sekar, “Neural Network Models for Recognition of Stop Consonant Vowel (SCV) Segments in Continuous Speech”, PhD Thesis, CSE Dept., IIT Madras, 1996
7. P. Kiran Kumar, “Texture Edge Extraction using One Dimensional Processing”, MS Thesis, CSE Dept., 2001
8. S. P. Kishore, “Speaker Verification using Autoassociative Neural Netwrok Models”, MS Thesis, CSE Dept., IIT Madras, 2000
9. B. Yegnanarayana, K. Sharath Reddy and S. P. Kishore, “Source and System Features for Speaker Recognition using AANN Models”, ICASSP, May 2001
10. S. P. Kishore, Suryakanth V. Ganagashetty and B. Yegnanarayana, “Online Text Independent Speaker Verification System using Autoassociative Neural Network Models”, INNS-IEEE Int. Conf. Neural Networks, July 2001.
11. K. Sharat Reddy, “Source and System Features for Speaker Recognition”, MS Thesis, CSE Dept., IIT Madras, September 2001.
12. B. Yegnanarayana and S. P. Kishore, “Autoassociative Neural Networks: An alternative to GMM for Pattern Recognition”, to appear in Nerual Networks 2002.