Graph-based Polyphonic Multitrack Music Generation
Emanuele Cosenza, Andrea Valenti, Davide Bacciu

TL;DR
This paper introduces a novel graph-based deep learning model for polyphonic multitrack music generation, enabling hierarchical structure-content separation and conditional instrument control, resulting in realistic and musically coherent compositions.
Contribution
It proposes a new graph representation for music and a hierarchical Variational Autoencoder that models structure and content separately, enhancing controllability and interpretability in music generation.
Findings
Generated music is tonally and rhythmically consistent.
Model can interpolate between musical sequences realistically.
Latent space organizes according to musical concepts.
Abstract
Graphs can be leveraged to model polyphonic multitrack symbolic music, where notes, chords and entire sections may be linked at different levels of the musical hierarchy by tonal and rhythmic relationships. Nonetheless, there is a lack of works that consider graph representations in the context of deep learning systems for music generation. This paper bridges this gap by introducing a novel graph representation for music and a deep Variational Autoencoder that generates the structure and the content of musical graphs separately, one after the other, with a hierarchical architecture that matches the structural priors of music. By separating the structure and content of musical graphs, it is possible to condition generation by specifying which instruments are played at certain times. This opens the door to a new form of human-computer interaction in the context of music co-creation. After…
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Taxonomy
TopicsMusic Technology and Sound Studies · Music and Audio Processing · Neuroscience and Music Perception
