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.

 本研究针对现有自动音乐转录技术中的性能不足问题，提出了一种新颖的离散去噪扩散模型架构，专注于钢琴转录。通过引入邻域注意力层作为去噪模块以及独特的训练和推理状态转换策略，实验表明该模型在MAESTRO数据集上实现了超过以往扩散模型和基线模型的转录表现，显示出显著的提升潜力。 

D3RM：一种用于钢琴转录的离散去噪扩散精炼模型