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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.04899 |
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| _version_ | 1866915641388171264 |
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| author | Kobayashi, Mutsumi Watanabe, Hiroshi |
| author_facet | Kobayashi, Mutsumi Watanabe, Hiroshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning and composing of classical music using restricted Boltzmann machines Kobayashi, Mutsumi Watanabe, Hiroshi Sound Machine Learning Audio and Speech Processing 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. |
| title | Learning and composing of classical music using restricted Boltzmann machines |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.04899 |