{"type":"ScholarlyArticle","title":"METEOR: Melody-aware Texture-controllable Symbolic Music Re-Orchestration via Transformer VAE","authors":[{"name":"Dinh-Viet-Toan Le","slug":"dinh-viet-toan-le","given":"Dinh-Viet-Toan","family":"Le","affiliation":""},{"name":"Yi-Hsuan Yang","slug":"yi-hsuan-yang","given":"Yi-Hsuan","family":"Yang","affiliation":""}],"abstract":"Re-orchestration is the process of adapting a music piece for a different set of instruments. By altering the original instrumentation, the orchestrator often modifies the musical texture while preserving a recognizable melodic line and ensures that each part is playable within the technical and expressive capabilities of the chosen instruments.\r\n    \r\nIn this work, we propose METEOR, a model for generating Melody-aware Texture-controllable re-Orchestration with a Transformer-based variational auto-encoder (VAE). This model performs symbolic instrumental and textural music style transfers with a focus on melodic fidelity and controllability. We allow bar- and track-level controllability of the accompaniment with various textural attributes while keeping a homophonic texture. With both subjective and objective evaluations, we show that our model outperforms style transfer models on a re-orchestration task in terms of generation quality and controllability. Moreover, it can be adapted for a lead sheet orchestration task as a zero-shot learning model, achieving performance comparable to a model specifically trained for this task.","doi":"10.24963/ijcai.2025/1125","url":"https://ijcai.org/proceedings/2025/1125","pdf":"https://ijcai.org/proceedings/2025/1125.pdf","markdown":"https://ijcai.org/proceedings/2025/1125.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1125","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI, Arts & Creativity","track":"AI, Arts & Creativity","number":1125,"pages":{"first":10126,"last":10134},"keywords":["Methods and resources: AI systems for collaboration and co-creation","Methods and resources: Machine learning, deep learning, neural models, reinforcement learning","Application domains: Music and sound"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}