{"type":"ScholarlyArticle","title":"Q&A: Query-Based Representation Learning for Multi-Track Symbolic Music re-Arrangement","authors":[{"name":"Jingwei Zhao","slug":"jingwei-zhao","given":"Jingwei","family":"Zhao","affiliation":"Institute of Data Science , NUS; Integrative Sciences and Engineering Programme , NUS Graduate School"},{"name":"Gus Xia","slug":"gus-xia","given":"Gus","family":"Xia","affiliation":"Music X Lab , NYU Shanghai"},{"name":"Ye Wang","slug":"ye-wang","given":"Ye","family":"Wang","affiliation":"School of Computing , NUS; Institute of Data Science , NUS; Integrative Sciences and Engineering Programme , NUS Graduate School"}],"abstract":"Music rearrangement is a common music practice of reconstructing and reconceptualizing a piece using new composition or instrumentation styles, which is also an important task of automatic music generation. Existing studies typically model the mapping from a source piece to a target piece via supervised learning. In this paper, we tackle rearrangement problems via self-supervised learning, in which the mapping styles can be regarded as conditions and controlled in a flexible way. Specifically, we are inspired by the representation disentanglement idea and propose Q&A, a query-based algorithm for multi-track music rearrangement under an encoder-decoder framework. Q&A learns both a content representation from the mixture and function (style) representations from each individual track, while the latter queries the former in order to rearrange a new piece. Our current model focuses on popular music and provides a controllable pathway to four scenarios: 1) re-instrumentation, 2) piano cover generation, 3) orchestration, and 4) voice separation. Experiments show that our query system achieves high-quality rearrangement results with delicate multi-track structures, significantly outperforming the baselines.","doi":"10.24963/ijcai.2023/652","url":"https://ijcai.org/proceedings/2023/652","pdf":"https://ijcai.org/proceedings/2023/0652.pdf","markdown":"https://ijcai.org/proceedings/2023/652.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/652","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI and Arts","track":"AI and Arts","number":652,"pages":{"first":5878,"last":5886},"keywords":["Theory and philosophy of arts and creativity in AI systems: Autonomous creative or artistic AI","Methods and resources: Machine learning, deep learning, neural models, reinforcement learning","Application domains: Music and sound"],"videos":[],"references":[{"position":1,"raw":"[Couturier et al., 2022a] Louis Couturier, Louis Bigo, and Florence Levé. 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