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Computer-Generated Music
Chmelka, Jakub ; Polok, Lukáš (referee) ; Fapšo, Michal (advisor)
This bachelor´s thesis deals with the problem of music composition by means of computers. The system uses artificial neural networks which learn the regularities of the chosen music genre from the recordings in MIDI format. In addition to the melody, this work places great emphasis on rhythm of newly created compositions. Besides the problem of neural networks, this work deals with the appropriate representation of input data and re-conversion into MIDI format. The system is implemented as a set of scripts mainly in the mathematical software Matlab.
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Computer-Generated Music
Lanc, Jakub ; Štancl, Vít (referee) ; Fapšo, Michal (advisor)
Main topic of the thesis is musical composition by means of computer, specifically usage of neural networks for melody generation. Contemporary artificial music composition approaches are briefly mapped, and some types of neural networks used are presented. Implementation of Matlab scripts, allowing melody generation is presented, and the process is illustrated on a few examples.
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Artificial Composition of Multi-Instrumental Polyphonic Music
Samuel, David ; Pilát, Martin (advisor) ; Neruda, Roman (referee)
David Samuel We propose a generative model for artificial composition of both classical and popular music with the goal of producing music as well as humans do. The problem is that music is based on a highly sophisticated hierarchical structure and it is hard to measure its quality automatically. Contrary to other's work, we try to generate a symbolic representation of music with multiple different instruments playing simultaneously to cover a broader musical space. We train three modules based on LSTM networks to generate the music; a lot of effort is put into reducing high complexity of multi-instrumental music representation by a thorough musical analysis. Our work serves mainly as a proof-of-concept for music composition. We believe that the proposed preprocessing techniques and symbolic representation constitute a useful resource for future research in this field. 1
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Statistical machine learning with applications in music
Janásková, Eliška ; Večeř, Jan (advisor) ; Hlávka, Zdeněk (referee)
The aim of this thesis is to train a computer on Beatles' songs using the re- search project Magenta from the Google Brain Team to produce its own music, to derive backpropagation formulas for recurrent neural networks with LSTM cells used in the Magenta music composing model, to overview machine learning techniques and discuss its similarities with methods of mathematical statistics. In order to explore the qualities of the artificially composed music more thor- oughly, we restrict ourselves to monophonic melodies only. We train three deep learning models with three different configurations (Basic, Lookback, and At- tention) and compare generated results. Even though the artificially composed music is not as interesting as the original Beatles, it is quite likeable. According to our analysis based on musically informed metrics, artificial melodies differ from the original ones especially in lengths of notes and in pitch differences be- tween consecutive notes. The artificially composed melodies tend to use shorter notes and higher pitch differences. 1
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Statistical machine learning with applications in music
Janásková, Eliška ; Večeř, Jan (advisor) ; Hlávka, Zdeněk (referee)
The aim of this thesis is to review the current state of machine learning in music composition and to train a computer on Beatles' songs using research project Magenta from the Google Brain Team to produce its own music. In order to explore the qualities of the generated music more thoroughly, we restrict our- selves to monophonic melodies only. We train three deep learning models with three different configurations (Basic, Lookback, and Attention) and compare generated results. Even though the generated music is not as interesting as the original Beatles, it is quite likable. According to our analysis based on musically informed metrics, generated melodies differ from the original ones especially in lengths of notes and in pitch differences between consecutive notes. Generated melodies tend to use shorter notes and higher pitch differences. In theoretical background, we cover the most commonly used machine learning algorithms, introduce neural networks and review related work of music generation. 1
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Artificial Composition of Multi-Instrumental Polyphonic Music
Samuel, David ; Pilát, Martin (advisor) ; Neruda, Roman (referee)
David Samuel We propose a generative model for artificial composition of both classical and popular music with the goal of producing music as well as humans do. The problem is that music is based on a highly sophisticated hierarchical structure and it is hard to measure its quality automatically. Contrary to other's work, we try to generate a symbolic representation of music with multiple different instruments playing simultaneously to cover a broader musical space. We train three modules based on LSTM networks to generate the music; a lot of effort is put into reducing high complexity of multi-instrumental music representation by a thorough musical analysis. Our work serves mainly as a proof-of-concept for music composition. We believe that the proposed preprocessing techniques and symbolic representation constitute a useful resource for future research in this field. 1
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Music composition based on a programming language
Pavlín, Tomáš ; Maršík, Ladislav (advisor) ; Hajič, Jan (referee)
Computer music composition brings a lot of problems which can be solved using a variety of approaches. The existing music composition programs either do not provide enough flexibility to composers or they are considerably complicated for users which do not have technical background. In this thesis, we introduce an intuitive programming language designed for music composition along with an interpreter of this language represented by user-friendly graphical interface. The interface can be utilized for music composition and production even by users without technical and musical skills. The program provides a new approach for music composition and allows an effortless music creation that can be used e.g. in game industry. In addition, the program can be used for musical accompaniment. 1
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Computer-Generated Music
Chmelka, Jakub ; Polok, Lukáš (referee) ; Fapšo, Michal (advisor)
This bachelor´s thesis deals with the problem of music composition by means of computers. The system uses artificial neural networks which learn the regularities of the chosen music genre from the recordings in MIDI format. In addition to the melody, this work places great emphasis on rhythm of newly created compositions. Besides the problem of neural networks, this work deals with the appropriate representation of input data and re-conversion into MIDI format. The system is implemented as a set of scripts mainly in the mathematical software Matlab.
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Computer-Generated Music
Lanc, Jakub ; Štancl, Vít (referee) ; Fapšo, Michal (advisor)
Main topic of the thesis is musical composition by means of computer, specifically usage of neural networks for melody generation. Contemporary artificial music composition approaches are briefly mapped, and some types of neural networks used are presented. Implementation of Matlab scripts, allowing melody generation is presented, and the process is illustrated on a few examples.
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