Abstract / truncated to 115 words (read the full abstract)

The burgeoning of deep learning-based music generation has overlooked the potential of symbolic representations tailored for fretted instruments. Guitar tablatures offer an advantageous approach to represent prescriptive information about music performance, often missing from standard MIDI representations. This dissertation tackles a gap in symbolic music generation by developing models that predict both musical structures and expressive guitar performance techniques. We first present DadaGP, a dataset comprising over 25k songs converted from the Guitar Pro tablature format to a dedicated token format suiting sequence models such as the Transformer. To establish a benchmark, we first introduce a baseline unconditional model for guitar tablature generation, by training a Transformer-XL architecture on the DadaGP dataset. We explored various ... toggle 6 keywords

deep learning – music information retrieval – artificial intelligence – generative models – guitar – tablatures

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