A Survey of Music Generation in the Context of Interaction
<|reference_start|>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 (eg. generating a Bach-style chorale) or style transfer (eg. 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.<|reference_end|>
@article{agchar2024a,
    title={A Survey of Music Generation in the Context of Interaction},
    author={Ismael Agchar, Ilja Baumann, Franziska Braun, Paula Andrea Perez-Toro,
  Korbinian Riedhammer, Sebastian Trump, Martin Ullrich},
    journal={arXiv preprint arXiv:2402.15294},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15294},
    primaryClass={cs.SD cs.AI cs.LG eess.AS}
}