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Music Improvisation
Angelov, Michael ; Hradiš, Michal (referee) ; Fapšo, Michal (advisor)
The thesis deals with problems concerning algorithmic music compositon, especially the domain of musical improvisation. There is an opening presentation of some of existing tools and approaches that are commonly used in domain of computer music. Consenquently there is a proposal of a new system, using main principles of markov chains and prediction suffix trees (PST) with description of its implementation. The main task of developped application is to analyze an external MIDI recording that is proposed to the system by user and create a new and inovative musical material in MIDI format that would sound close to the original recording giving an impression of a computer improvised music to the listener.
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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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Music Improvisation
Angelov, Michael ; Hradiš, Michal (referee) ; Fapšo, Michal (advisor)
The thesis deals with problems concerning algorithmic music compositon, especially the domain of musical improvisation. There is an opening presentation of some of existing tools and approaches that are commonly used in domain of computer music. Consenquently there is a proposal of a new system, using main principles of markov chains and prediction suffix trees (PST) with description of its implementation. The main task of developped application is to analyze an external MIDI recording that is proposed to the system by user and create a new and inovative musical material in MIDI format that would sound close to the original recording giving an impression of a computer improvised music to the listener.
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