Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910010354696192 |
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| author | He, Zhanhong Meng, Hanyu Huang, David Togneri, Roberto |
| author_facet | He, Zhanhong Meng, Hanyu Huang, David Togneri, Roberto |
| contents | Estimating piano dynamic from audio recordings is a fundamental challenge in computational music analysis. In this paper, we propose an efficient multi-task network that jointly predicts dynamic levels, change points, beats, and downbeats from a shared latent representation. These four targets form the metrical structure of dynamics in the music score. Inspired by recent vocal dynamic research, we use a multi-scale network as the backbone, which takes Bark-scale specific loudness as the input feature. Compared to log-Mel as input, this reduces model size from 14.7 M to 0.5 M, enabling long sequential input. We use a 60-second audio length in audio segmentation, which doubled the length of beat tracking commonly used. Evaluated on the public MazurkaBL dataset, our model achieves state-of-the-art results across all tasks. This work sets a new benchmark for piano dynamic estimation and delivers a powerful and compact tool, paving the way for large-scale, resource-efficient analysis of musical expression. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_18190 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network He, Zhanhong Meng, Hanyu Huang, David Togneri, Roberto Audio and Speech Processing Machine Learning Sound H.5.5; I.2.6; I.5.4 Estimating piano dynamic from audio recordings is a fundamental challenge in computational music analysis. In this paper, we propose an efficient multi-task network that jointly predicts dynamic levels, change points, beats, and downbeats from a shared latent representation. These four targets form the metrical structure of dynamics in the music score. Inspired by recent vocal dynamic research, we use a multi-scale network as the backbone, which takes Bark-scale specific loudness as the input feature. Compared to log-Mel as input, this reduces model size from 14.7 M to 0.5 M, enabling long sequential input. We use a 60-second audio length in audio segmentation, which doubled the length of beat tracking commonly used. Evaluated on the public MazurkaBL dataset, our model achieves state-of-the-art results across all tasks. This work sets a new benchmark for piano dynamic estimation and delivers a powerful and compact tool, paving the way for large-scale, resource-efficient analysis of musical expression. |
| title | Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network |
| topic | Audio and Speech Processing Machine Learning Sound H.5.5; I.2.6; I.5.4 |
| url | https://arxiv.org/abs/2510.18190 |