AI 中文总结
P-MUSE是统一器乐合成与编辑的MIDI-to-Music框架，通过多阶段课程学习整合两种生成范式，提出相位感知引导策略与尾端丢弃法，并建立含四类乐器的综合基准。
AI 中文摘要
MIDI-to-Music系统可将目标MIDI序列的旋律与节奏渲染为音乐片段，同时从提示录音中克隆乐器音色。现有系统通常采用两种不同范式之一：仅基于提示音频的条件生成，适用于缺少对齐提示MIDI的场景；以及基于配对提示音频与MIDI的上下文学习，利用跨模态对齐实现对MIDI跟随与音色相似度的更强控制。我们引入P-MUSE，这一器乐MIDI-to-Music框架通过支持提示-MIDI可选输入的多阶段课程学习，统一了上述两种范式。P-MUSE还通过共享的中间填充公式统一了音乐生成与局部编辑。基于理论分析与实证研究，我们为转录到音频系统提出了相位感知的无分类器引导调度原则，以及尾端丢弃（Tail-Drop）策略。最后，为推动该领域研究，我们建立了首个综合基准，涵盖多种提示模式、生成/编辑任务，以及四种代表性乐器：钢琴、吉他、贝斯与鼓。演示可访问此https URL查看。
英文摘要
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/.
