A Survey of Music Generation in the Context of Interaction

  • In recent years, machine learning, and in particular generative adversarial neural networks (GANs) and attention-based neural networks (transformers), have been successfully used to compose and generate music, both melodies and polyphonic pieces. Current research focuses foremost on style replication (e.g., generating a Bach-style chorale) or style transfer (e.g., classical to jazz) based on large amounts of recorded or transcribed music, which in turn also allows for fairly straight-forward “performance” evaluation. However, most of these models are not suitable for human-machine co-creation through live interaction, neither is clear, how such models and resulting creations would be evaluated. This article presents a thorough review of music representation, feature analysis, heuristic algorithms, statistical and parametric modelling, and human and automatic evaluation measures, along with a discussion of which approaches and models seem most suitable for live interaction.

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Author:Ismael Agchar, Ilja BaumannORCiD, Franziska Braun, Paula Andrea Perez-Toro, Korbinian RiedhammerORCiD, Sebastian Trump, Martin Ullrich
DOI:https://doi.org/10.48550/arXiv.2402.15294
ArXiv Id:http://arxiv.org/abs/2402.15294
Document Type:Article
Language:English
Reviewed:Begutachtet/Reviewed
Release Date:2025/02/17
Pagenumber:47
institutes:Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
Licence (German):Creative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International
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