13 Zentrale Universitätseinrichtungen
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/14
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Item Open Access Sampling music spaces with generative AI(2025) Braun, KaiRecent advances in deep learning have led to powerful models for symbolic music generation, but their creative potential often comes at the expense of user controllability. To provide a more controllable environment for AI-assisted composing, we present a visual approach based on latent embeddings from models, creating an interactive two-dimensional music space in which users can generate melodies in desired areas. By applying dimensionality reduction to embeddings from state-of-the-art symbolic music generation models, melodies are mapped into a scatterplot where proximity reflects similarity. We develop an interactive framework where users can generate, select, and explore melodies directly in the music space. Within this framework, we implement several generation techniques based on the MusicVAE, Pop Music Transformer (REMI), and FIGARO models, and evaluate their effectiveness in filling user-defined regions of the space. Quantitative results show that MusicVAE’s similar and interpolate methods most reliably generate samples close to the targeted area, while REMI and FIGARO produce greater diversity at the cost of precision. A qualitative analysis further highlights how dimensionality reduction methods, parameter settings, and spatial density influence the outcome of generation. Results show that techniques based on the MusicVAE model generate melodies that are both in close proximity and musically similar to their surroundings, making it the most applicable method to generate samples in desired areas of the music space. Our work contributes to the development of visual, interactive methods for human-AI co-creativity in music, emphasizing controllability, exploration, and inspiration in the composition process.