用小模型同时预测钢琴力度与节拍结构，效率更高。
Joint Estimation of Piano Dynamics and Metrical Structure with a Multi-task Multi-Scale Network
- 多任务多尺度网络共享特征表示，联合预测力度、变化点、节拍和强拍。
- 输入采用Bark尺度响度特征，模型大小仅0.5M，支持60秒长音频处理。
- 在MazurkaBL数据集上四项指标均达当前最优，适合大规模音乐表达分析。
从音频中估计钢琴力度是计算音乐分析中的基础挑战。本文提出一种高效的多任务网络，通过共享潜在表示联合预测力度水平、力度变化点、节拍和强拍，这些目标共同构成乐谱中力度的节律结构。受近期声乐力度研究启发，采用多尺度网络作为主干，输入为Bark尺度特定响度特征。相比传统log-Mel输入，模型规模从14.7M降至0.5M，支持长序列输入。音频分段长度达60秒，是常规节拍追踪长度的两倍。在公开的MazurkaBL数据集上，该模型在所有任务上均取得当前最佳性能。本工作为钢琴力度估计设立了新基准，提供了一个强大且紧凑的工具，推动了大规模、资源高效音乐表现分析的发展。
原文摘要 · Abstract (English)
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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