Statistical machine learning with applications in music
| Název práce v češtině: | Statistické strojové učení s aplikacemi v hudbě |
|---|---|
| Název v anglickém jazyce: | Statistical machine learning with applications in music |
| Klíčová slova: | strojové učení, tensor flow, hudební skladba, neuronové sítě s LSTM, hodnocení hudby |
| Klíčová slova anglicky: | machine learning, tensor flow, music composition, neural networks with LSTM, evaluation of music |
| Akademický rok vypsání: | 2018/2019 |
| Typ práce: | diplomová práce |
| Jazyk práce: | angličtina |
| Ústav: | Katedra pravděpodobnosti a matematické statistiky (32-KPMS) |
| Vedoucí / školitel: | doc. RNDr. Jan Večeř, Ph.D. |
| Řešitel: | skrytý - zadáno a potvrzeno stud. odd. |
| Datum přihlášení: | 23.10.2018 |
| Datum zadání: | 23.10.2018 |
| Datum potvrzení stud. oddělením: | 19.11.2018 |
| Datum a čas obhajoby: | 12.06.2019 08:00 |
| Datum odevzdání elektronické podoby: | 09.05.2019 |
| Datum odevzdání tištěné podoby: | 10.05.2019 |
| Datum proběhlé obhajoby: | 12.06.2019 |
| Oponenti: | doc. RNDr. Zdeněk Hlávka, Ph.D. |
| Zásady pro vypracování |
| This work aims to review the current state of the art in statistical machine learning and apply it to music composition. The thesis should first briefly describe the general methods of statistical machine learning. Second, thesis should survey the existing methods applied in machine generated music. The current mainstream methods of statistical machine learning are widely implemented in Python, so the author of the thesis should get familiar with this programming language and the respective machine learning libraries, namely Scikit-Learn and Tensor Flow. In addition, Google Brain Team has recently released Magenta, a research project working with Tensor Flow aimed at machine learning applications for arts. Lastly, the work should train the computer to produce its own compositions based on a training data. The training music data are expected to be input as MIDI files from a selected set of existing music compositions. More specifically, one may focus on a small number of music composers in order to train the computer system to a more narrow data set, which can arguably lead to more pleasing outcomes. A possible set of training data can come from The Beatles songs or from a selected set of guitar solos, which are all well documented.
This thesis requires specific skills from the author, namely he/she should be both capable of working with data, but also should have a minimal understanding of music. |
| Seznam odborné literatury |
| Efron, Hastie: Computer Age Statistical Inference, Cambridge University Press, 2016
Muller, Guido: Introduction to Machine Learning with Python, O'Reilly, 2016 Geron: Hands-on Machine Learning with Scikit-Learn and Tensor Flow, O'Reilly, 2017 The Beatles: Complete Scores, Hal Leonard, 1993 |
| Předběžná náplň práce |
| Vytvořte hudbu se statistickým strojovým učením. |
| Předběžná náplň práce v anglickém jazyce |
| Create music with statistical machine learning. |