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arXiv · 2402.15294

A Survey of Music Generation in the Context of Interaction

Ismael Agchar·Ilja Baumann·Franziska Braun·Paula Andrea Perez-Toro·Korbinian Riedhammer·Sebastian Trump·Martin Ullrich
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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 (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.

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Ismael Agchar, Ilja Baumann, Franziska Braun, Paula Andrea Perez-Toro, Korbinian Riedhammer, Sebastian Trump, Martin Ullrich. 2024-02-23. A Survey of Music Generation in the Context of Interaction. https://arxiv.org/abs/2402.15294

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