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Primerjava postopkov globokega učenja za samodejno tvorjenje glasbe
ID Turk, Tom (Author), ID Štruc, Vitomir (Mentor) More about this mentor... This link opens in a new window

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Abstract
V diplomskem delu smo preizkusili možnosti ustvarjanja samodejno tvorjene glasbe z uporabo različnih vrst nevronskih mrež (RNN, CNN, LSTM, GRU). Uporabili smo orodja Google Magenta, Google Wavenet in GRUV. Za uporabo orodja Google Magenta smo morali zvočne podatke pretvoriti v MIDI in MXL predstavitve zvočnih datotek, medtem ko Wavenet in GRUV sprejmeta surove zvočne podatke. Nevronske mreže smo učili s pomočjo množice podatkov elektronske glasbe ter množice podatkov jazz glasbe. Na koncu je sledila evalvacija rezultatov, izvedli smo slušne teste in analizirali rezultate.

Language:Slovenian
Keywords:samodejno tvorjena glasba, nevronske mreže, RNN, CNN, LSTM, GRU
Work type:Bachelor thesis/paper
Organization:FE - Faculty of Electrical Engineering
Year:2018
PID:20.500.12556/RUL-102746 This link opens in a new window
Publication date in RUL:07.09.2018
Views:1196
Downloads:278
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Secondary language

Language:English
Title:Comparative analysis of deep learning models for automatic music generation
Abstract:
The thesis addresses the possibility of the automatic music generation, using a variety of neural network topologies (RNN, CNN, LSTM, GRU). In this thesis, the capabilities of projects Google Magenta, Google Wavenet and GRUV were explored. Google Magenta operates on the MIDI and MXL files, so we had to convert the files from the source datasets to the MIDI and MXL files first, whereas Google Magenta and Google Wavenet operate on the raw audio files. Listed neural networks were trained on a dataset, containing samples of electronic music, and a jazz music samples dataset. Finally, we evaluated the results using an auditory test and analyzed the results.

Keywords:generative music, neural networks, RNN, CNN, LSTM, GRU

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