Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network
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.
Keywords
Cite
@article{arxiv.2510.18190,
title = {Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network},
author = {Zhanhong He and Hanyu Meng and David Huang and Roberto Togneri},
journal= {arXiv preprint arXiv:2510.18190},
year = {2026}
}
Comments
Accepted to ICASSP2026 conference