Abstract
Expressive performance rendering (EPR) and automatic piano transcription (APT) are fundamental yet inverse tasks in music information retrieval: EPR generates expressive performances from symbolic scores, while APT recovers scores from performances. Despite their dual nature, prior work has addressed them independently. In this paper we propose a unified framework that jointly models EPR and APT by disentangling note-level score content and global performance style representations from both paired and unpaired data. Our framework is built on a transformer-based sequence-to-sequence architecture and is trained using only sequence-aligned data, without requiring fine-grained note-level alignment. To automate the rendering process while ensuring stylistic compatibility with the score, we introduce an independent diffusion-based performance style recommendation module that generates style embeddings directly from score content. This modular component supports both style transfer and flexible rendering across a range of expressive styles. Experimental results from both objective and subjective evaluations demonstrate that our framework achieves competitive performance on EPR and APT tasks, while enabling effective content-style disentanglement, reliable style transfer, and stylistically appropriate rendering. Demos are available at this https URL
Abstract (translated)
以下是该段落的中文翻译： 表现性表演渲染（EPR）和自动钢琴转录（APT）是音乐信息检索中的基本且互为逆向的任务：EPR 从符号乐谱生成具有表现力的演奏，而 APT 则是从演奏中恢复乐谱。尽管它们的本质相反，之前的研究工作分别独立地处理这些问题。在本文中，我们提出了一种统一框架，通过分离配对和非配对数据中的音符级乐谱内容和全局表演风格表示来共同建模 EPR 和 APT。我们的框架基于转换器（transformer）的序列到序列架构，并仅使用序列对齐的数据进行训练，无需精细的音符级别对齐。为了在确保与乐谱风格一致的情况下自动化渲染过程，我们引入了一个独立的基于扩散模型的表演风格推荐模块，该模块直接从乐谱内容生成风格嵌入。这一模块化组件支持跨多种表现性风格的内容转换和灵活渲染。实验结果表明，在客观评估和主观评估中，我们的框架在 EPR 和 APT 任务上实现了具有竞争力的表现，并且能够有效地实现内容与风格的分离、可靠的风格转移以及符合风格的渲染。演示可在该链接查看：[此链接处应填写实际提供的演示网址]
URL
https://arxiv.org/abs/2509.23878
