轻量级微调方法，提升音乐节拍追踪精度
HingeNet: A Harmonic-Aware Fine-Tuning Approach for Beat Tracking
- 通过铰链结构接入预训练模型中间特征，实现高效微调
- 在标准数据集上达到当前最优节拍追踪效果
- 特别关注谐波信息，适合音频分析与音乐理解任务
微调预训练基础模型在音乐信息检索中取得显著进展，但其在节拍追踪任务中的应用仍受限于标注数据不足，传统微调方法效果不佳。为此，我们提出HingeNet，一种专为节拍追踪设计的新型通用参数高效微调方法。该方法为轻量且可分离的网络结构，外观类似铰链，通过使用预训练模型的中间特征表示作为输入，紧密集成于基础模型之上。这一独特架构赋予HingeNet广泛的泛化能力，可有效适配多种预训练模型。此外，考虑到谐波在节拍追踪中的重要性，我们在微调过程中引入谐波感知机制，以更好地捕捉和强调音乐信号中的谐波结构。在基准数据集上的实验表明，HingeNet在节拍与强拍追踪任务中均达到当前最优性能。
原文摘要 · Abstract (English)
Fine-tuning pre-trained foundation models has made significant progress in music information retrieval. However, applying these models to beat tracking tasks remains unexplored as the limited annotated data renders conventional fine-tuning methods ineffective. To address this challenge, we propose HingeNet, a novel and general parameter-efficient fine-tuning method specifically designed for beat tracking tasks. HingeNet is a lightweight and separable network, visually resembling a hinge, designed to tightly interface with pre-trained foundation models by using their intermediate feature representations as input. This unique architecture grants HingeNet broad generalizability, enabling effective integration with various pre-trained foundation models. Furthermore, considering the significance of harmonics in beat tracking, we introduce harmonic-aware mechanism during the fine-tuning process to better capture and emphasize the harmonic structures in musical signals. Experiments on benchmark datasets demonstrate that HingeNet achieves state-of-the-art performance in beat and downbeat tracking
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