dc.contributor.author | Bhandari, K | en_US |
dc.contributor.author | Colton, S | en_US |
dc.contributor.author | Artificial Intelligence in Music, Sound, Art and Design – 13th International Conference, EvoMUSART 2024 | en_US |
dc.contributor.editor | Johnson, C | en_US |
dc.contributor.editor | Rebelo, S | en_US |
dc.contributor.editor | Santos, I | en_US |
dc.date.accessioned | 2024-02-12T08:48:49Z | |
dc.date.available | 2024-01-11 | en_US |
dc.date.issued | 29-03-2024 | |
dc.identifier.citation | Bhandari, K., Colton, S. (2024). Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation. In: Johnson, C., Rebelo, S.M., Santos, I. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024. Lecture Notes in Computer Science, vol 14633. Springer, Cham. https://doi.org/10.1007/978-3-031-56992-0_3 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/94584 | |
dc.description.abstract | Modelling musical structure is vital yet challenging for artificial intelligence systems that generate symbolic music compositions. This literature review dissects the evolution of techniques for incorporating coherent structure, from symbolic approaches to foundational and transformative deep learning methods that harness the power of computation and data across a wide variety of training paradigms. In the later stages, we review an emerging technique which we refer to as “sub-task decomposition" that involves decomposing music generation into separate high-level structural planning and content creation stages. Such systems incorporate some form of musical knowledge or neuro-symbolic methods by extracting melodic skeletons or structural templates to guide the generation. Progress is evident in capturing motifs and repetitions across all three eras reviewed, yet modelling the nuanced development of themes across extended compositions in the style of human composers remains difficult. We outline several key future directions to realize the synergistic benefits of combining approaches from all eras examined. | en_US |
dc.publisher | A Springer Nature Computer Science book series (CCIS, LNAI, LNBI, LNBIP or LNCS) | en_US |
dc.rights | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://doi.org/10.1007/978-3-031-56992-0_3 | |
dc.subject | Generative Music | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Computerized Music | en_US |
dc.title | Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation | en_US |
dc.type | Conference Proceeding | |
dc.rights.holder | © 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG | |
dc.identifier.doi | doi.org/10.1007/978-3-031-56992-0_3 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
dcterms.dateAccepted | 2024-01-11 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | UKRI Centre for Doctoral Training in Artificial Intelligence and Music::Engineering and Physical Sciences Research Council | en_US |