A methodology for automatic composition of polyphonic music - Ribeiro, Angelica Abadia Paulista; Rosa, João Luís Garcia (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2025: Anais do XIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2025.247513, 2025)
Automatic music composition presents challenges that exceed those of picture or video synthesis, requiring precise temporal modeling and the synchronization of several instrumental voices with unique dynamics. This study presents three Generative Adversarial Network (GAN) architectures—the Improvisation, Composition, and Hybrid models—for the automated generation of polyphonic notation for multiple instruments. Utilizing a corpus of over 100,000 symphonic music pieces in Wave, MIDI, and MusicXML formats (including a dataset curated by the primary author), the models include completely extracted MIDI features derived from the conversion of all Wave files to MIDI. Assessment via quantitative intra- and inter-instrument metrics, supplemented by a musician-centric study, demonstrates that the models can create music from inception, produce coherent four-measure excerpts, and engage in human-machine co-composition to transform single-instrument melodies into comprehensive arrangements.