{"ID":2851977,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18190","arxiv_id":"2510.18190","title":"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.","short_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 structu...","url_abs":"https://arxiv.org/abs/2510.18190","url_pdf":"https://arxiv.org/pdf/2510.18190v2","authors":"[\"Zhanhong He\",\"Hanyu Meng\",\"David Huang\",\"Roberto Togneri\"]","published":"2025-10-21T00:32:13Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.LG\",\"cs.SD\"]","methods":"[]","has_code":false}