Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network

Zhanhong He; Hanyu Meng; David Huang; Roberto Togneri

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arXiv · 2510.18190

Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network

Zhanhong He·Hanyu Meng·David Huang·Roberto Togneri

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Abstract

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.

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Zhanhong He, Hanyu Meng, David Huang, Roberto Togneri. 2025-10-21. Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network. https://arxiv.org/abs/2510.18190

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