Abstract
Music structure segmentation is a key task in audio analysis, but existing models perform poorly on Electronic Dance Music (EDM). This problem exists because most approaches rely on lyrical or harmonic similarity, which works well for pop music but not for EDM. EDM structure is instead defined by changes in energy, rhythm, and timbre, with different sections such as buildup, drop, and breakdown. We introduce EDMFormer, a transformer model that combines self-supervised audio embeddings using an EDM-specific dataset and taxonomy. We release this dataset as EDM-98: a group of 98 professionally annotated EDM tracks. EDMFormer improves boundary detection and section labelling compared to existing models, particularly for drops and buildups. The results suggest that combining learned representations with genre-specific data and structural priors is effective for EDM and could be applied to other specialized music genres or broader audio domains.
Abstract (translated)
音乐结构分割是音频分析中的关键任务，但现有的模型在处理电子舞曲（EDM）时表现不佳。这一问题的原因在于大多数方法依赖于歌词或和声的相似性，这种方法对流行音乐有效，但对于EDM并不适用。EDM的结构则由能量、节奏和音色的变化定义，并且具有不同的部分，如蓄力段、爆发段和间奏。 我们引入了EDMFormer模型，这是一个结合自监督音频嵌入并使用特定于EDM的数据集与分类法的Transformer模型。我们将这一数据集作为EDM-98发布：一组共98首专业标注的EDM曲目。相比于现有的模型，EDMFormer在边界检测和段落标记方面有了显著改进，特别是在爆发段和蓄力段的表现更为突出。 实验结果表明，将学习到的表示与特定流派的数据及结构先验相结合是处理EDM的有效方法，并且这一策略也可以应用于其他专门化的音乐类型或更广泛的音频领域。
URL
https://arxiv.org/abs/2603.08759
