Life-Like Artificial Music: Understanding the Impact of AI on Musical Thinking – Nikita Braguinski

Abstract:

This lecture explores the impact of machine learning on the future of music research and theory. It argues that AI-generated music poses a deep challenge for existing theories: AI systems can learn to imitate musical styles without receiving any information about human music theory concepts, raising questions about the validity of those concepts. Additionally, music-generating AI systems can be trained on audio directly, bypassing notation, while human music theory almost always works with notation as a simplified and abstracted proxy.

As an example of the conceptual challenges and shifts that now arise in music research, the talk examines a recent paper that compares Western music theory concepts with structures that emerge in a machine learning model trained on musical notation. While the paper finds similarities between the two, the talk argues that the machine learning system’s output is still influenced by human biases and choices in the training data and model architecture – and that this influence may in fact be unavoidable.

Finally, the talk argues that while AI may be able to generate novel structures for analyzing music, their applicability to human music theory and practice may prove to be extremely limited due to the differences between human cognition and machine learning. Overall, the talk raises questions about the future potential for AI to disrupt human theory-making – and not only in the discipline of musicology.

No knowledge of musicological concepts is required for understanding the presentation and participating in the discussion.

This event is part of our summer semester 2024 Lecture Series Lifelikeness.

If you would like to attend, please write a short email to events@khk.rwth-aachen.de.

Dr. Nikita Braguinski is a 2023-2024 Fellow at the Käte Hamburger Kolleg “Cultures of Research” at RWTH Aachen University. In his work he currently concentrates on the possible impact of machine learning and big online listening datasets on the future of music research. His book “Mathematical Music. From Antiquity to Music AI” (Routledge, 2022) was translated into Korean, receiving the Sejong book prize in 2023. He was a Fellow at Harvard University, a Visiting Scholar at the University of Cambridge, and a Researcher at Humboldt University of Berlin with funding from the Volkswagen Foundation. In 2023, he co-convened, together with Eamonn Bell and Miriam Akkermann, the ZiF Bielefeld Visiting Research Group “The Future of Musical Knowledge in the Age of Machine Learning”.