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Algorithmic Music Composition
 Pankuch, Adam ; Beran, Vítězslav (oponent) ; Kolář, Martin (vedoucí práce)
The goal of this thesis is to create a system, which is able to generate guitar tracks. This problem consists of two main parts: acquisition of a training dataset and training of a suitable deep learning model. The first part of the problem was solved by series of scripts which filter and transform a set of songs with many instruments in Guitar Pro format to a set of guitar tracks in pianoroll format. The second part of the problem was solved by training a few convolutional and recurrent neural networks on the created dataset of guitar tracks. Guitar tracks generated by these networks were compared to each other and evaluated. Although, the generated tracks are not very harmonic and pleasing to the ear, they show that convolutional networks are more suitable for generation of polyphonic music than other types of neural networks.
Úplný záznam
Algorithmic Music Composition
 Pankuch, Adam ; Beran, Vítězslav (oponent) ; Kolář, Martin (vedoucí práce)
The goal of this thesis is to create a system, which is able to generate guitar tracks. This problem consists of two main parts: acquisition of a training dataset and training of a suitable deep learning model. The first part of the problem was solved by series of scripts which filter and transform a set of songs with many instruments in Guitar Pro format to a set of guitar tracks in pianoroll format. The second part of the problem was solved by training a few convolutional and recurrent neural networks on the created dataset of guitar tracks. Guitar tracks generated by these networks were compared to each other and evaluated. Although, the generated tracks are not very harmonic and pleasing to the ear, they show that convolutional networks are more suitable for generation of polyphonic music than other types of neural networks.
Úplný záznam

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