{"ID":2874157,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.04899","arxiv_id":"2509.04899","title":"Learning and composing of classical music using restricted Boltzmann machines","abstract":"We investigate how machine learning models acquire the ability to compose music and how musical information is internally represented within such models. We develop a composition algorithm based on a restricted Boltzmann machine (RBM), a simple generative model capable of producing musical pieces of arbitrary length. We convert musical scores into piano-roll image representations and train the RBM in an unsupervised manner. We confirm that the trained RBM can generate new musical pieces; however, by analyzing the model's responses and internal structure, we find that the learned information is not stored in a form directly interpretable by humans. This study contributes to a better understanding of how machine learning models capable of music composition may internally represent musical structure and highlights issues related to the interpretability of generative models in creative tasks.","short_abstract":"We investigate how machine learning models acquire the ability to compose music and how musical information is internally represented within such models. We develop a composition algorithm based on a restricted Boltzmann machine (RBM), a simple generative model capable of producing musical pieces of arbitrary length. W...","url_abs":"https://arxiv.org/abs/2509.04899","url_pdf":"https://arxiv.org/pdf/2509.04899v3","authors":"[\"Mutsumi Kobayashi\",\"Hiroshi Watanabe\"]","published":"2025-09-05T08:18:14Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.LG\",\"eess.AS\"]","methods":"[]","has_code":false}