用离散扩散模型提升钢琴乐谱转录精度，效果优于现有方法。
D3RM: A Discrete Denoising Diffusion Refinement Model for Piano Transcription
- 采用邻域注意力模块进行逐步去噪，生成高分辨率乐谱
- 在MAESTRO数据集上F1得分超越先前扩散模型与基线
- 适合音乐信息学、自动作曲等场景的高精度转录需求
扩散模型因在复杂数据分布建模上的优异表现，广泛应用于生成任务，并在图像分割等判别任务中展现出竞争力。尽管已有人探索其在自动音乐转录中的应用，但性能仍未达到理想水平。本文聚焦于离散扩散模型的精炼能力，提出一种新颖的钢琴转录架构。模型利用邻域注意力层作为去噪模块，基于预训练声学模型微调后的特征，逐步预测目标高分辨率钢琴卷。为增强精炼效果，设计了一种在训练与推理阶段使用不同转移状态的新策略。在MAESTRO数据集上的实验表明，该方法在F1分数上优于此前基于扩散模型的钢琴转录方法及基线模型。代码已公开于https://github.com/hanshounsu/d3rm。
原文摘要 · Abstract (English)
Diffusion models have been widely used in the generative domain due to their convincing performance in modeling complex data distributions. Moreover, they have shown competitive results on discriminative tasks, such as image segmentation. While diffusion models have also been explored for automatic music transcription, their performance has yet to reach a competitive level. In this paper, we focus on discrete diffusion model's refinement capabilities and present a novel architecture for piano transcription. Our model utilizes Neighborhood Attention layers as the denoising module, gradually predicting the target high-resolution piano roll, conditioned on the finetuned features of a pretrained acoustic model. To further enhance refinement, we devise a novel strategy which applies distinct transition states during training and inference stage of discrete diffusion models. Experiments on the MAESTRO dataset show that our approach outperforms previous diffusion-based piano transcription models and the baseline model in terms of F1 score. Our code is available in https://github.com/hanshounsu/d3rm.
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