  Autoregressive models are now capable of generating high-quality minute-long
expressive MIDI piano performances. Even though this progress suggests new
tools to assist music composition, we observe that generative algorithms are
still not widely used by artists due to the limited control they offer,
prohibitive inference times or the lack of integration within musicians'
workflows. In this work, we present the Piano Inpainting Application (PIA), a
generative model focused on inpainting piano performances, as we believe that
this elementary operation (restoring missing parts of a piano performance)
encourages human-machine interaction and opens up new ways to approach music
composition. Our approach relies on an encoder-decoder Linear Transformer
architecture trained on a novel representation for MIDI piano performances
termed Structured MIDI Encoding. By uncovering an interesting synergy between
Linear Transformers and our inpainting task, we are able to efficiently inpaint
contiguous regions of a piano performance, which makes our model suitable for
interactive and responsive A.I.-assisted composition. Finally, we introduce our
freely-available Ableton Live PIA plugin, which allows musicians to smoothly
generate or modify any MIDI clip using PIA within a widely-used professional
Digital Audio Workstation.
