Abstract
Generating 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.
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References
Chen, S., Zhong, Y., Du, R.: Automatic composition of guzheng (Chinese zither) music using long short-term memory network (LSTM) and reinforcement learning (RL). Sci. Rep. 12(1), 15829 (2022)
Collins, N.: Reinforcement learning for live musical agents. In: ICMC (2008)
Dadman, S., Bremdal, B.A., Bang, B., Dalmo, R.: Toward interactive music generation: a position paper. IEEE Access 10, 125679–125695 (2022)
Dittenbach, M., Merkl, D., Rauber, A.: The growing hierarchical self-organizing map. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000, Neural Computing: New Challenges and Perspectives for the New Millennium, vol. 6, pp. 15–19. IEEE (2000)
Dong, H.W., Hsiao, W.Y., Yang, L.C., Yang, Y.H.: MuseGAN: multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Dupont, S., Ravet, T., Picard-Limpens, C., Frisson, C.: Nonlinear dimensionality reduction approaches applied to music and textural sounds. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2013)
Fortunato, M., et al.: Noisy networks for exploration. arXiv preprint arXiv:1706.10295 (2017)
Huang, C.Z.A., et al.: Music transformer. arXiv preprint arXiv:1809.04281 (2018)
Jaques, N., Gu, S., Turner, R.E., Eck, D.: Tuning recurrent neural networks with reinforcement learning (2017)
Ji, S., Yang, X., Luo, J., Li, J.: RL-chord: CLSTM-based melody harmonization using deep reinforcement learning. IEEE Trans. Neural Netw. Learn. Syst. (2023)
Liu, H., Xie, X., Ruzi, R., Wang, L., Yan, N.: RE-RLtuner: a topic-based music generation method. In: 2021 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 1139–1142. IEEE (2021)
Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)
Pál, T., Várkonyi, D.T.: Comparison of dimensionality reduction techniques on audio signals. In: ITAT, pp. 161–168 (2020)
Roberts, A., Engel, J., Raffel, C., Hawthorne, C., Eck, D.: A hierarchical latent vector model for learning long-term structure in music. In: International Conference on Machine Learning, pp. 4364–4373. PMLR (2018)
Smith, B.D., Garnett, G.E.: Reinforcement learning and the creative, automated music improviser. In: Machado, P., Romero, J., Carballal, A. (eds.) EvoMUSART 2012. LNCS, vol. 7247, pp. 223–234. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29142-5_20
Veale, T., Cardoso, F.A.: Computational Creativity: The Philosophy and Engineering of Autonomously Creative Systems. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-319-43610-4
Wooldridge, M.J.: An Introduction to Multiagent Systems, 2nd edn. Wiley, Chichester (2009)
Wu, S.L., Yang, Y.H.: MuseMorphose: full-song and fine-grained music style transfer with one transformer VAE. arXiv preprint arXiv:2105.04090 (2021)
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Dadman, S., Bremdal, B.A. (2023). Multi-agent Reinforcement Learning for Structured Symbolic Music Generation. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science(), vol 13955. Springer, Cham. https://doi.org/10.1007/978-3-031-37616-0_5
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