Intern
    Data Science Chair

    Deep Music Composition

    07.06.2023

    Recent transformer-based generative language models have achieved near human-like performance in generating natural text. In this work, the applicability of these models to deep music generation is investigated.

    The scope of this work includes the evaluation of different representation methods for symbolic music and their impact on quality. While the topic generally focuses on symbolic music (MIDI), musical genre, instruments, and musical complexity can be varied.

    Supervisor: Daniel Schlör

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