Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network
This work addresses the problem of computational music analysis for piano dynamics, providing a compact and efficient tool for large-scale analysis, though it is incremental in building on existing vocal dynamic research.
The paper tackles the challenge of estimating piano dynamics from audio recordings by proposing a multi-task network that jointly predicts dynamic levels, change points, beats, and downbeats, achieving state-of-the-art results on the MazurkaBL dataset.
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.