Pattern Analysis
Transition Recognition
Signal level artificial neural network
by (c) Ville Turjanmaa
Transition recognition neural network is a pattern learning and
recognizing network, which can be applied to notated music and other
patterns. The network shows hierarchial dependencies between
occurred patterns. Transition ( TR ) neural network collects
data without loss of information.
Can be applied to any time-related pattern.
Uses unsupervised learning.
Shows time-related hierarchial dependencies.
No loss of information.
Example case
This document forms a TR -network from
the following example. Chords can be used instead of single
notes.
Information from the note sequence
1) Quantize level
Quantize level is the period of shortest event in
score. In the above example, quantize level is 1/16 note.
2) Number of input cells for the network.
The above example ( GEDE-CEDE ) uses four different notes, generating input
cells G , E , D and C.
Transition connections and recognizing cell
Transition example for first two notes in score.
A transition cell has two input nodes ( A and B ), output
line and internal state ( 0 or 1 ). When
signal arrives to node A, the internal state raises from
0 to 1. When input node B receives a signal while the internal state
is 1, the cell fires and returns to internal state 0.
When receiving a pair ( ie. 'CC' ), the first pair raises the
internal state to 1, second pair fires the cell and keeps
the state at 1,
since other pairs could follow. All other cases should zero the
cells internal state.
When firing, all cells
in TR-network generate one impulse to the next level. The input data
should be synchronized with hidden level cells. When forming a
network, only one cell is generated for each transition.
Impulse response table for transition recognizing cell:
A B State -> State Out
0 0 0 0 0
0 0 1 0 0
0 1 0 0 0
0 1 1 0 1
1 0 0 1 0
1 0 1 1 0
1 1 0 1 0
1 1 1 1 1
Transition neural network
When any of the first hidden level cells fire, the input is
a two note sequence from the example. When any of the
second hidden level cells fire, the input is a three note sequence
from the example.
Transition map
All occurring patterns ( 25 ) with hierarchial dependencies
Conclusion
Transition neural network can be used as a general tool for
notation.
TR-network learns and recognizes patterns without loss of information.
The structure of formed network shows all dependencies between patterns.
Both learning and recognizing takes place in same cell.
In addition to notation, transition neural network ( TRNN ) can be
applied to other time-related data streams, like sentences and functions.
Downloads
Ms-dos program which forms TR network: TR.ZIP
Notes for Bach BWV 847.1 Page 1 and
Page 2
Transition recognition neural network is a pattern learning and recognizing network, which can be applied to notated music and other patterns. The network shows hierarchial dependencies between occurred patterns. Transition ( TR ) neural network collects data without loss of information.
Can be applied to any time-related pattern.
Uses unsupervised learning.
Shows time-related hierarchial dependencies.
No loss of information.
Example case
This document forms a TR -network from the following example. Chords can be used instead of single notes.
Information from the note sequence
1) Quantize levelQuantize level is the period of shortest event in score. In the above example, quantize level is 1/16 note.
2) Number of input cells for the network.
The above example ( GEDE-CEDE ) uses four different notes, generating input cells G , E , D and C.
Transition connections and recognizing cell
Transition example for first two notes in score.
A transition cell has two input nodes ( A and B ), output line and internal state ( 0 or 1 ). When signal arrives to node A, the internal state raises from 0 to 1. When input node B receives a signal while the internal state is 1, the cell fires and returns to internal state 0. When receiving a pair ( ie. 'CC' ), the first pair raises the internal state to 1, second pair fires the cell and keeps the state at 1, since other pairs could follow. All other cases should zero the cells internal state.
When firing, all cells in TR-network generate one impulse to the next level. The input data should be synchronized with hidden level cells. When forming a network, only one cell is generated for each transition.
Impulse response table for transition recognizing cell:
A B State -> State Out
0 0 0 0 0
0 0 1 0 0
0 1 0 0 0
0 1 1 0 1
1 0 0 1 0
1 0 1 1 0
1 1 0 1 0
1 1 1 1 1
Transition neural network
When any of the first hidden level cells fire, the input is a two note sequence from the example. When any of the second hidden level cells fire, the input is a three note sequence from the example.
Transition map
All occurring patterns ( 25 ) with hierarchial dependencies
Conclusion
Transition neural network can be used as a general tool for notation. TR-network learns and recognizes patterns without loss of information. The structure of formed network shows all dependencies between patterns. Both learning and recognizing takes place in same cell.In addition to notation, transition neural network ( TRNN ) can be applied to other time-related data streams, like sentences and functions.
Downloads
Ms-dos program which forms TR network: TR.ZIPNotes for Bach BWV 847.1 Page 1 and Page 2
