A Distribution Matching Approach to Neural Piano Transcription with Optimal Transport
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914575305146368 |
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| author | Wei, Weixing Lalang, Raynaldi Li, Dichucheng Yoshii, Kazuyoshi |
| author_facet | Wei, Weixing Lalang, Raynaldi Li, Dichucheng Yoshii, Kazuyoshi |
| contents | 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 music.Our 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17405 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Distribution Matching Approach to Neural Piano Transcription with Optimal Transport Wei, Weixing Lalang, Raynaldi Li, Dichucheng Yoshii, Kazuyoshi Sound Multimedia 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 music.Our 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. |
| title | A Distribution Matching Approach to Neural Piano Transcription with Optimal Transport |
| topic | Sound Multimedia |
| url | https://arxiv.org/abs/2605.17405 |