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Neural models for recognition of basic units of semiographic chants
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Semiographic chantMaterials and methods
Results
Neural models for recognition of basic units ofsemiographic chants
AIST'2014
Ekaterina Vylomova1
Andrey Philippovich2 Marina Danshina2 Irina Golubeva2
Yury Philippovich2
Montclair State University, Montclair, USA
Bauman Moscow State Technical University, Moscow, Russia
April 10-12, 2014
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
A semiographic chant
Flags,or znamyas (semiographic symbols) meaning musical
symbols;
Text matching �ags;
Pometas indicating the duration and amplitude of the music.
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
DataFeature selectionClassi�cation
Data
A list of pometas
Pometa Training set Test set Usage frequency
"Ñ" 97 28 0.25
"Ð" 78 24 0.16
"Í" 68 24 0.21
"Ì" 65 20 0.25
"Ï" 62 20 0.12
"Ã" 31 10 0.07
"Â" 28 9 0.04
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
DataFeature selectionClassi�cation
Features
(a) Pometa "M".Extra white spaceand noise removed
(b) Intersection withhorizontal lines(1-2-2-2-1)
(c) Vertical linesadded(1-1-1-1-1)
Extracting geometrical features
Initial features
Number of intersections in the horizontal plane(Nh = 5)
Number of intersections in the vertical plane(Nv = 5)
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
DataFeature selectionClassi�cation
Extended feature set
3 additional features
Number of inclined lines used in the pometa
Number of horizontal lines used in the pometa
Number of vertical lines used in the pometa
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
DataFeature selectionClassi�cation
Clustering results(MDS)
(a) Clustering (10 features) (b) Clustering (13 features)
Clustering results
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
DataFeature selectionClassi�cation
Example of feature vector
Pometa "M"
Each pometa is described as a vector with 13 dimensions (5+5+3).
Pometa "M"might be xi = (1; 2; 2; 2; 1; 1; 1; 1; 1; 1; 1; 4; 0; 0).
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
DataFeature selectionClassi�cation
Approaches
Types of classi�ers
multilayer perceptron
probabilistic neural network
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
DataFeature selectionClassi�cation
Multilayer perceptron
Multilayer perceptron
Parameters
Input layer: 14 neurons, hidden layer: 15 neurons, output layer: 7
(binary codes for each class of pometas).
Activation function is set to sigmoid.
Learning rate: 0.9, momentum: 0.1
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
DataFeature selectionClassi�cation
Probabilistic neural network
A probabilistic neural network
Parameters
Input layer: 13 neurons, examples layer: 294(number of examples),
summation layer: 7(number of classes)
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
(a) ROC curve for MLP(13features)
(b) ROC curve for PNN (13features)
E�ciency comparison
Correctness
MSE: Err = 1
2
∑K
k=1(dk − yk)
2
MLPtrain = 0.96,MLPtest = 0.92, PNNtrain = 0.96, PNNtest = 0.93.
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
Conclusion
PNN is better
better results
less error
easier to use(not so many con�guration parameters)
trains faster
bad news:needs a lot of memory to store examples :(
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
Conclusion
Overall
investigated the problem of recognition of basic units of
ancient Russian chants.
proposed possible feature set that could be used to train a
classi�er.
compared and evaluated the error rates for 2 di�erent
classi�cation techniques: multilayer perceptron with
back-propagation algorithm and probabilistic neural network.
showed the bene�ts of the latter model, e.g. less error rate,
less dependency on network settings set.
proved that PNNs exhibit better behaviour in pattern
recognition tasks with few training examples.
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants
Semiographic chantMaterials and methods
Results
Thank you!
Questions?
Ekaterina Vylomova, Andrey Philippovich, Marina Danshina, Irina Golubeva, Yury PhilippovichRecognition of basic units of semiographic chants