Noise-to-Notes: Diffusion-based Generation and Refinement for Automatic Drum Transcription

• URL: http://arxiv.org/abs/2509.21739v1
• Date: Fri, 26 Sep 2025 01:12:43 GMT
• Title: Noise-to-Notes: Diffusion-based Generation and Refinement for Automatic Drum Transcription
• Authors: Michael Yeung, Keisuke Toyama, Toya Teramoto, Shusuke Takahashi, Tamaki Kojima,
• Abstract summary: Automatic drum transcription (ADT) is traditionally formulated as a discriminative task to predict drum events from audio spectrograms.<n>Noise-to-Notes (N2N) is a framework leveraging diffusion modeling to transform audio-conditioned Gaussian noise into drum events with associated velocities.<n>N2N establishes a new state-of-the-art performance across multiple ADT benchmarks.
• Score: 6.453619274330351
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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