Interpreting Graphic Notation with MusicLDM: An AI Improvisation of Cornelius Cardew's Treatise

Autor:	Karchkhadze, Tornike, Shao, Keren, Dubnov, Shlomo
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Zdroj:	2024 IEEE International Conference on Big Data (Big Data)
Druh dokumentu:	Working Paper
Popis:	This work presents a novel method for composing and improvising music inspired by Cornelius Cardew's Treatise, using AI to bridge graphic notation and musical expression. By leveraging OpenAI's ChatGPT to interpret the abstract visual elements of Treatise, we convert these graphical images into descriptive textual prompts. These prompts are then input into MusicLDM, a pre-trained latent diffusion model designed for music generation. We introduce a technique called "outpainting," which overlaps sections of AI-generated music to create a seamless and cohesive composition. We demostrate a new perspective on performing and interpreting graphic scores, showing how AI can transform visual stimuli into sound and expand the creative possibilities in contemporary/experimental music composition. Musical pieces are available at https://bit.ly/TreatiseAI
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.08944 Zobrazit plný text záznamu View this record from Arxiv