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Object Recognition by Neural Networks
Marák, Jaroslav ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis is focused on neural networks and their classification capability in object recognition tasks. For recognition is there used neural networks with feedforward architecture which is learned by Back Propagation algorithm. We discusses about problems which appear while a choosing topology of network or using various lerning-significant parametters while a learning process. Achieved results are presented in experiments with estimation.
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Algorithmic Accompaniment Composition
Vinš, Jakub ; Hradiš, Michal (referee) ; Kolář, Martin (advisor)
This thesis deals with problems of computer music, especially with generating accompaniment to an existing song in MIDI format by means of artificial neural networks. Existing methods of algorithmic music composition are presented in the beginning. Followed by problems and their solutions connected with the conversion of MIDI files to matrices, which are suitable as an input for neural network and their inverse transformation. Subsequently are proposed, created, optimized and evaluated models which generate saxophone and piano accompaniment by means of feedforward and recurrent neural network. At the end model generates accompaniment to my own song as a form of a test.
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Algorithmic Accompaniment Composition
Vinš, Jakub ; Hradiš, Michal (referee) ; Kolář, Martin (advisor)
This thesis deals with problems of computer music, especially with generating accompaniment to an existing song in MIDI format by means of artificial neural networks. Existing methods of algorithmic music composition are presented in the beginning. Followed by problems and their solutions connected with the conversion of MIDI files to matrices, which are suitable as an input for neural network and their inverse transformation. Subsequently are proposed, created, optimized and evaluated models which generate saxophone and piano accompaniment by means of feedforward and recurrent neural network. At the end model generates accompaniment to my own song as a form of a test.
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|
|
Object Recognition by Neural Networks
Marák, Jaroslav ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis is focused on neural networks and their classification capability in object recognition tasks. For recognition is there used neural networks with feedforward architecture which is learned by Back Propagation algorithm. We discusses about problems which appear while a choosing topology of network or using various lerning-significant parametters while a learning process. Achieved results are presented in experiments with estimation.
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