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dc.contributor.authorDadman, Shayan
dc.contributor.authorBremdal, Bernt Arild
dc.date.accessioned2024-04-03T08:37:40Z
dc.date.available2024-04-03T08:37:40Z
dc.date.issued2023-07-12
dc.description.abstractGenerating structured music using deep learning methods with symbolic representation is challenging due to the complex relationships between musical elements that define a musical composition. Symbolic representation of music, such as MIDI or sheet music, can help overcome some of these challenges by encoding the music in a format that allows manipulation and analysis. However, the symbolic representation of music still requires interpretation and understanding of musical concepts and theory. In this paper, we propose a method for symbolic music generation using a multi-agent structure built on top of growing hierarchical self-organizing maps and recurrent neural networks. Our model primarily focuses on music structure. It operates at a higher level of abstraction, enabling it to capture longer-term musical structure and dependency. Our approach involves using reinforcement learning as a self-learning method for agents and the human user as a musical expert to facilitate the agents’ learning of global dependency and musical characteristics. We show how agents can learn and adapt to the user’s preferences and musical style. Furthermore, we present and discuss the potential of our approach for agent communication, learning and adaptation, and distributed problem-solving in music generation.en_US
dc.identifier.citationDadman S, Bremdal BA: Multi-agent Reinforcement Learning for Structured Symbolic Music Generation. In: Mathieu P, Dignum F, Novais P, De la Prieta F. Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023., 2023. Springer p. 52-63en_US
dc.identifier.cristinIDFRIDAID 2251733
dc.identifier.isbn978-3-031-37616-0
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttps://hdl.handle.net/10037/33311
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.titleMulti-agent Reinforcement Learning for Structured Symbolic Music Generationen_US
dc.type.versionacceptedVersionen_US
dc.typeChapteren_US
dc.typeBokkapittelen_US


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