# P-MUSE: Prompt-MIDI-Optional Model for Unified Instrumental Music Synthesis and Editing

arXiv: https://arxiv.org/abs/2608.01920 · HTML: https://mlsanity.com/paper/2608.01920/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2026.

## Built on by (most cited first, 2 of 2)

- [CPR: Combining global composing, local performing and full-sequence refining in piano rendering with continuous autoregressive modelling](https://mlsanity.com/paper/2609.18216/) (2026)
- [Synthesis and editing of multi-instrument audio mixtures using scalar-quantised latents with MIDI Span conditioning](https://mlsanity.com/paper/2609.25546/) (2026)

## Abstract (arXiv)

MIDI-to-Music system renders the melody and rhythm of a target MIDI sequence into musical segment while cloning instrument timbre from a prompt recording. Existing systems typically adopt one of two distinct paradigms: conditional generation with prompt audio alone, which remains applicable when aligned prompt MIDI is unavailable, and In-Context Learning with paired prompt audio and MIDI, which exploits cross-modal alignment for stronger control on MIDI following and timbre similarity. We introduce P-MUSE, an instrumental MIDI-to-Music framework that unifies both paradigms via a multi-stage Curriculum-Learning supporting prompt-MIDI-optional inputs. P-MUSE further unifies music generation and local editing through a shared fill-in-the-middle formulation. Grounded in theoretical analysis and empirical study, we propose a phase-aware classifier-free guidance scheduling principle for Transcription-to-Audio systems, alongside a Tail-Drop strategy. Finally, to advance research in this field, we establish the first comprehensive benchmark, covering various prompt modes, generation/editing tasks, and four representative instruments: piano, guitar, bass, and drums. Demos are available at https://p-muse.github.io/.