{"type":"ScholarlyArticle","title":"MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models","authors":[{"name":"Yixiao Zhang","slug":"yixiao-zhang","given":"Yixiao","family":"Zhang","affiliation":"University of London"},{"name":"Yukara Ikemiya","slug":"yukara-ikemiya","given":"Yukara","family":"Ikemiya","affiliation":"Sony AI"},{"name":"Gus Xia","slug":"gus-xia","given":"Gus","family":"Xia","affiliation":"Music X Lab , MBZUAI"},{"name":"Naoki Murata","slug":"naoki-murata","given":"Naoki","family":"Murata","affiliation":"Sony AI"},{"name":"Marco A. Martínez-Ramírez","slug":"marco-a-martinez-ramirez","given":"Marco A.","family":"Martínez-Ramírez","affiliation":"Sony AI"},{"name":"Wei-Hsiang Liao","slug":"wei-hsiang-liao","given":"Wei-Hsiang","family":"Liao","affiliation":"Sony AI"},{"name":"Yuki Mitsufuji","slug":"yuki-mitsufuji","given":"Yuki","family":"Mitsufuji","affiliation":"Sony AI"},{"name":"Simon Dixon","slug":"simon-dixon","given":"Simon","family":"Dixon","affiliation":"University of London"}],"abstract":"Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, the task of editing these generated music remains a significant challenge. This paper introduces a novel approach to edit music generated by such models, enabling the modification of specific attributes, such as genre, mood, and instrument, while maintaining other aspects unchanged. Our method transforms text editing to the latent space manipulation, and adds an additional constraint to enforce consistency. It seamlessly integrates with existing pretrained text-to-music diffusion models without requiring additional training. Experimental results demonstrate superior performance over both zero-shot and certain supervised baselines in style and timbre transfer evaluations. We also show the practical applicability of our approach in real-world music editing scenarios.","doi":"10.24963/ijcai.2024/864","url":"https://ijcai.org/proceedings/2024/864","pdf":"https://ijcai.org/proceedings/2024/0864.pdf","markdown":"https://ijcai.org/proceedings/2024/864.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/864","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":864,"pages":{"first":7805,"last":7813},"keywords":["Application domains: Music and sound","Methods and resources: Machine learning, deep learning, neural models, reinforcement learning"],"videos":[],"references":[{"position":1,"raw":"[Agostinelli et al., 2023] Andrea Agostinelli, Timo I Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, et al. 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