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A Distribution Matching Approach to Neural Piano Transcription with Optimal Transport

2026-05-17 12:01:44
Weixing Wei, Raynaldi Lalang, Dichucheng Li, Kazuyoshi Yoshii
arXiv_SD
                 
Abstract

This paper describes a novel paradigm that formalizes automatic piano transcription (APT) as an optimal transport (OT) problem, not as a frame-level multi-label binary classification problem. Our method learns to minimize the cost of transporting a predicted distribution of note events to the ground-truth distribution over time and frequency. The OT loss can thus accommodate temporal misalignment, leading to perceptually relevant optimization. We also propose a convolutional recurrent neural network (CRNN) with a harmonics-aware attention mechanism to capture the spectro-temporal dependencies inherent in this http URL experiments using the MAESTRO dataset showed that our method attained a state-of-the-art performance in onset detection. We confirmed the versatility of the OT loss in application to existing models.

Abstract (translated)

URL

https://arxiv.org/abs/2605.17405

PDF

https://arxiv.org/pdf/2605.17405.pdf

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