@article{AgcharBaumannBraunetal.2024, author = {Agchar, Ismael and Baumann, Ilja and Braun, Franziska and Perez-Toro, Paula Andrea and Riedhammer, Korbinian and Trump, Sebastian and Ullrich, Martin}, title = {A Survey of Music Generation in the Context of Interaction}, doi = {10.48550/arXiv.2402.15294}, pages = {47}, year = {2024}, abstract = {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.}, language = {en} }