Neural models for recognition of basic units of semiographic chants

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


Results

A semiographic chant

Flags,or znamyas (semiographic symbols) meaning musical

symbols;

Text matching �ags;

Pometas indicating the duration and amplitude of the music.



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



Results


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)



Results


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



Results


Clustering results(MDS)

(a) Clustering (10 features) (b) Clustering (13 features)

Clustering results



Results


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).



Results


Approaches

Types of classi�ers

multilayer perceptron

probabilistic neural network



Results


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



Results


Probabilistic neural network

A probabilistic neural network

Parameters

Input layer: 13 neurons, examples layer: 294(number of examples),

summation layer: 7(number of classes)



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.



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 :(



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.



Results

Thank you!

Questions?


Data & Analytics

Neural models for recognition of basic units of semiographic chants