CONFERENCE SECRETARIAT
Meetx is the conference bureau handling the secretariat for the digitalizeinsthlm.
E-mail: digitalizeinstockholm@meetx.se
IMPORTANT DATES
Conference Date: 17 October 2024
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Computer Scientist and Associate Professor at KTH Royal Institute of Technology
Musicologist and Researcher in MUSAiC
Preparing for the coming flood of AI-generated music
Our world will soon be awash with music generated by artificial intelligence (AI) that is impossible to distinguish by sound alone from music created by people. Algorithmic approaches to music composition have been topics of interest for centuries; however, the contemporary confluence of digitized and accessible music data, along with computational resources and efficient machine learning algorithms, are making possible the wholesale creation of new music audio catalogues at a fraction of the cost of human labor and intellectual property rights.
The implications of this predicament are significant, and so there is a need for major interdisciplinary efforts to prepare. Bob and Elin will demonstrate some of the latest developments in generative AI applied to music. They will also discuss their call for developing a critical study of AI music involving perspectives and methodologies from multiple disciplines, including engineering, musicology, sociology, economics, and philosophy.
Bob Sturm is an Associate Professor of Computer Science at KTH Royal Institute of Technology (Speech, Music and Hearing). His background includes physics, engineering, and music composition (which has always been a major motivation for his technical pursuits). Bob is currently leading the major EU-funded project MUSAiC, which is about music at the frontiers of artificial creativity and criticism.
Co-presenter Elin Kanhov is a musicologist and postdoctoral researcher in MUSAiC, studying the prospects and challenges of AI as it develops in the music practice. She engages with perspectives from feminist post-humanities and critical theory. She is working on projects exploring tensions between AI and traditional music communities, more-than-human data ethics, and ethnographic studies of AI music service users.