Abstract
Audio-based music structure analysis (MSA) is an essential task in Music Information Retrieval that remains challenging due to the complexity and variability of musical form. Recent advances highlight the potential of fine-tuning pre-trained music foundation models for MSA tasks. However, these models are typically trained with high temporal feature resolution and short audio windows, which limits their efficiency and introduces bias when applied to long-form audio. This paper presents a temporal adaptation approach for fine-tuning music foundation models tailored to MSA. Our method enables efficient analysis of full-length songs in a single forward pass by incorporating two key strategies: (1) audio window extension and (2) low-resolution adaptation. Experiments on the Harmonix Set and RWC-Pop datasets show that our method significantly improves both boundary detection and structural function prediction, while maintaining comparable memory usage and inference speed.
Abstract (translated)
基于音频的音乐结构分析（MSA）是音乐信息检索中的一个核心任务，但由于音乐形式的复杂性和多变性，这一任务仍然具有挑战性。最近的研究进展表明，通过微调预训练的音乐基础模型来执行MSA任务具有很大潜力。然而，这些模型通常是在高时间特征分辨率和短音频窗口上进行训练的，这在应用于长曲目时会限制它们的效率并引入偏差。本文提出了一种针对MSA任务对音乐基础模型进行微调的时间适应方法。我们的方法通过采用两个关键策略：（1）扩展音频窗口；（2）低分辨率调整，在单次前向传递中实现了对完整长度歌曲的有效分析。在Harmonix Set和RWC-Pop数据集上的实验表明，与现有方法相比，我们的方法显著提高了边界检测和结构功能预测的准确性，并且保持了相似的记忆使用量和推理速度。
URL
https://arxiv.org/abs/2507.13572
