Abstract
Automatic drum transcription (ADT) is traditionally formulated as a discriminative task to predict drum events from audio spectrograms. In this work, we redefine ADT as a conditional generative task and introduce Noise-to-Notes (N2N), a framework leveraging diffusion modeling to transform audio-conditioned Gaussian noise into drum events with associated velocities. This generative diffusion approach offers distinct advantages, including a flexible speed-accuracy trade-off and strong inpainting capabilities. However, the generation of binary onset and continuous velocity values presents a challenge for diffusion models, and to overcome this, we introduce an Annealed Pseudo-Huber loss to facilitate effective joint optimization. Finally, to augment low-level spectrogram features, we propose incorporating features extracted from music foundation models (MFMs), which capture high-level semantic information and enhance robustness to out-of-domain drum audio. Experimental results demonstrate that including MFM features significantly improves robustness and N2N establishes a new state-of-the-art performance across multiple ADT benchmarks.
Abstract (translated)
自动鼓点转录（ADT）传统上被定义为一个判别性任务，即从音频频谱图中预测鼓点事件。在这项工作中，我们将ADT重新定义为一个条件生成性任务，并引入了Noise-to-Notes（N2N），这是一个框架，利用扩散模型将受音频调节的高斯噪声转化为带有相关速度值的鼓点事件。这种生成式扩散方法提供了灵活的速度与精度权衡和强大的修复能力等独特优势。然而，二进制起始时间和连续力度值的生成对扩散模型来说是一个挑战，为了解决这一问题，我们引入了渐变伪赫比格损失（Annealed Pseudo-Huber loss）以促进有效的联合优化。 最后，为了增强低级频谱图特征，我们建议加入由音乐基础模型（MFMs）提取的特征。这些模型捕捉高层次的语义信息，并增强了对域外鼓声音频的鲁棒性。实验结果表明，包含MFM特征显著提高了鲁棒性，而N2N在多个ADT基准测试中建立了新的性能标杆。
URL
https://arxiv.org/abs/2509.21739
