Collaborative AI in Music Composition: Human-AI Symbiosis in Creative Processes

Authors

DOI:

https://doi.org/10.35870/ijmsit.v5i1.4085

Keywords:

AI music composition, Human-AI collaboration, Creative symbiosis, Generative music models, Machine learning in art

Abstract

Artificial Intelligence (AI) has ushered in a revolutionary change that mixes creative abilities between human composers and computer-powered intelligence during musical composition. The investigation examines the musical application of collaborative AI which exists as an aid to composers by suggesting ideas and creating motifs alongside enhancing musical arrangements. OpenAI’s MuseNet alongside Google’s MusicLM brought about new generative model technologies which enable musicians to have real-time access to adaptive tools that interpret as well as transform musical concepts. Based on secondary research and case studies, the article examines human composer-AI system partnerships to explain how their combined work restructures artistic authorship and creative methods. The paper uses today's artists with AI support and collaborative works between different fields to demonstrate the partnership's core dynamics. The discussion explores two main elements about AI music production which are human involvement versus programming automation alongside understanding emotional integrity in synthetic musical compositions together with co-creative copyrights regulations. This research evaluates how partnership between humans and AI components transforms musical education along with the process of composition for those without a musical background while testing established artistic boundaries of genre classification and original content production. This research project depicts AI as an amplification force that generates human creativity rather than being considered disruptive by showing how intelligent feedback systems work together with human agents. Co-creation behavior in this hybrid method motivates a fresh depiction of musical expression which sparks explorations about art creation and authorship roles and identity function in the future.

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Author Biography

  • Sunish Vengathattil, Clarivate Analytics

    Sr. Director Software Engineering, Calivate Analytics, Philadelphia, United States of America.

References

Alarfaj, F. K., Malik, I., Khan, H. U., Almusallam, N., Ramzan, M., & Ahmed, M. (2022). Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms. IEEE Access, 10, 39700–39715. https://doi.org/10.1109/ACCESS.2022.3166891

Ardley, N. C. (2011). André gide, the piano and writing: Keys to a creative symbiosis. Forum for Modern Language Studies, 47(3), 275–288. https://doi.org/10.1093/fmls/cqr007

Bhatt, H., Shah, V., Shah, K., Shah, R., & Shah, M. (2023, August 1). State-of-the-art machine learning techniques for melanoma skin cancer detection and classification: a comprehensive review. Intelligent Medicine. Chinese Medical Association. https://doi.org/10.1016/j.imed.2022.08.004

Brusilovsky, P. (2024, March 1). AI in Education, Learner Control, and Human-AI Collaboration. International Journal of Artificial Intelligence in Education. Springer. https://doi.org/10.1007/s40593-023-00356-z

Bryan-Kinns, N., Zhang, B., Zhao, S., & Banar, B. (2024). Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI. Machine Intelligence Research, 21(1), 29–45. https://doi.org/10.1007/s11633-023-1457-1

Cabrera, A. A., Perer, A., & Hong, J. I. (2023). Improving Human-AI Collaboration With Descriptions of AI Behavior. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1). https://doi.org/10.1145/3579612

Cemgil, A. T., Kappen, H. J., & Barber, D. (2006). A generative model for music transcription. IEEE Transactions on Audio, Speech and Language Processing, 14(2), 679–694. https://doi.org/10.1109/TSA.2005.852985

Colak, Y., & Gulec, S. S. (2022). Schlomo Dov Goitein’s “Political” Symbiosis in the Secrets of Simon Ben Yohai: A Qur’anic Reappraisal for a Jewish Apocalyptic Source on the Reflecting of an Early Islamic Background. Bussecon Review of Social Sciences (2687-2285), 4(1), 01–10. https://doi.org/10.36096/brss.v4i1.314

Hou, K., Hou, T., & Cai, L. (2023). Exploring Trust in Human–AI Collaboration in the Context of Multiplayer Online Games. Systems, 11(5). https://doi.org/10.3390/systems11050217

Howard, F. (2021). Its like being back in GCSE art”—engaging with music, film-making and boardgames. Creative pedagogies within youth work education. Education Sciences, 11(8). https://doi.org/10.3390/educsci11080374

Hua, Y., Li, F., & Yang, S. (2022). Application of Support Vector Machine Model Based on Machine Learning in Art Teaching. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/7954589

Huang, C. F., & Huang, C. Y. (2022). CVAE-GAN Emotional AI Music System for Car Driving Safety. Intelligent Automation and Soft Computing, 32(3), 1939–1953. https://doi.org/10.32604/IASC.2022.017559

Imasato, N., Miyazawa, K., Duncan, C., & Nagai, T. (2023). Using a Language Model to Generate Music in Its Symbolic Domain While Controlling Its Perceived Emotion. IEEE Access, 11, 52412–52428. https://doi.org/10.1109/ACCESS.2023.3280603

Jiang, N., Liu, X., Liu, H., Lim, E. T. K., Tan, C. W., & Gu, J. (2023). Beyond AI-powered context-aware services: the role of human–AI collaboration. Industrial Management and Data Systems, 123(11), 2771–2802. https://doi.org/10.1108/IMDS-03-2022-0152

Kim, H. G. (2024). Emotion-Driven Music Composition using AI and User Feedback. Journal of System and Management Sciences, 14(2), 467–481. https://doi.org/10.33168/JSMS.2024.0229

Mahmud, B., Hong, G., & Fong, B. (2023). A Study of Human-AI Symbiosis for Creative Work: Recent Developments and Future Directions in Deep Learning. ACM Transactions on Multimedia Computing, Communications and Applications, 20(2). https://doi.org/10.1145/3542698

Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56(4), 3005–3054. https://doi.org/10.1007/s10462-022-10246-w

Murati, E. (2022). Language & Coding Creativity. Daedalus, 151(2), 156–167. https://doi.org/10.1162/DAED_a_01907

Onuh Matthew Ijiga, Idoko Peter Idoko, Lawrence Anebi Enyejo, Omachile Akoh, Solomon Ileanaju Ugbane, & Akan Ime Ibokette. (2024). Harmonizing the voices of AI: Exploring generative music models, voice cloning, and voice transfer for creative expression. World Journal of Advanced Engineering Technology and Sciences, 11(1), 372–394. https://doi.org/10.30574/wjaets.2024.11.1.0072

Prvulovic, D., Vogl, R., & Knees, P. (2022). ReStyle-MusicVAE: Enhancing User Control of Deep Generative Music Models with Expert Labeled Anchors. In UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (pp. 63–66). Association for Computing Machinery, Inc. https://doi.org/10.1145/3511047.3536412

Sturm, B. L., & Ben-Tal, O. (2017). Taking the models back to music practice: Evaluating generative transcription models built using deep learning. Journal of Creative Music Systems, 2, 1–29. https://doi.org/10.5920/jcms.2017.09

Tan, X., & Li, X. (2021). A Tutorial on AI Music Composition. In MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia (pp. 5678–5680). Association for Computing Machinery, Inc. https://doi.org/10.1145/3474085.3478875

Vossing, M., Kuhl, N., Lind, M., & Satzger, G. (2022). Designing Transparency for Effective Human-AI Collaboration. Information Systems Frontiers, 24(3), 877–895. https://doi.org/10.1007/s10796-022-10284-3

Wang, C., Tan, X. P., Tor, S. B., & Lim, C. S. (2020, December 1). Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing. Elsevier B.V. https://doi.org/10.1016/j.addma.2020.101538

Zhang, Y. (2023). Utilizing Computational Music Analysis and AI for Enhanced Music Composition: Exploring Pre- and Post-Analysis. Journal of Advanced Zoology, 44(S6), 1377–1390. https://doi.org/10.17762/jaz.v44is6.2470

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Published

2025-05-14

How to Cite

Vengathattil, S. (2025). Collaborative AI in Music Composition: Human-AI Symbiosis in Creative Processes. International Journal of Management Science and Information Technology, 5(1), 253-262. https://doi.org/10.35870/ijmsit.v5i1.4085

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