Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV
Authors: name: Christian Zhou-Zheng, name: Philippe Pasquier

Existing work in automatic music generation has primarily focused on
end-to-end systems that produce complete compositions or continuations.
However, because musical composition is typically an iterative process, such
systems make it difficult to engage in the back-and-forth between human and
machine that is essential to computer-assisted creativity. In this study, we
address the task of personalizable, multi-track, long-context, and controllable
symbolic music infilling to enhance the process of computer-assisted
composition. We present MIDI-RWKV, a novel model based on the RWKV-7 linear
architecture, to enable efficient and coherent musical cocreation on edge
devices. We also demonstrate that MIDI-RWKV admits an effective method of
finetuning its initial state for personalization in the very-low-sample regime.
We evaluate MIDI-RWKV and its state tuning on several quantitative and
qualitative metrics, and release model weights and code at
https://github.com/christianazinn/MIDI-RWKV.