A BROADCAST MODEL OF SPREAD OF DIGITAL MUSIC COMPOSITION AMONG ARTIFICIAL AUDIENCE

Date

2024-4-22

Author

KARABIYIK, EMRE

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This thesis explores a novel paradigm in the realm of digital music composition, presenting a comprehensive model that simulates the intricate social dynamics between composers, broadcasters, and artificial audiences. Employing advanced machine learning algorithms, the study investigates the evolution of compositions in a dynamic ecosystem where composers continually adapt their styles based on feedback from an artificial audience. Composers, acting as content creators, generate a diverse array of musical compositions. Broadcasters play a pivotal role by selecting and disseminating compositions. The artificial audience, designed to mimic human preferences, influences the trajectory of compositions through their collective feedback. As compositions circulate within this network, composers receive real-time data on audience preferences, prompting them to refine and tailor their artistic expressions. This research contributes to our understanding of the emergent social dynamics in the digital music landscape, shedding light on the interplay between creators, broadcasters, and audiences. By employing a broadcast model, this thesis not only explores the dissemination of digital music but also elucidates how artificial audiences and broadcast mediums shape and influence the evolution of artistic creations in the digital era.

Subject Keywords

digital music composition, artificial audience, broadcast model, machine learning algorithms, compositional evolution

URI

https://hdl.handle.net/11511/109527

Collections

Graduate School of Informatics, Thesis