通过分组分类器链建模音乐标签间的依赖关系，提升标注准确率。
Music Tagging with Classifier Group Chains
- 将标签按类别分组，分组内顺序预测，捕捉标签间依赖性。
- 在MTG-Jamendo数据集上，相比独立分类，平均F1提升2.3%。
- 适合需要理解标签关联性的音乐内容分析场景。
我们提出一种基于分类器链的音乐标签方法，以建模音乐标签之间的相互作用。传统方法将多个标签视为独立的二分类问题，忽略了标签间的条件依赖，导致性能不佳。与大多数音乐标签器不同，该方法基于分类器链思想，按顺序估计每个标签。进一步地，将多个标签按类别（如流派）分组，以组为单位进行链式预测，称为分类器组链。该方法可建模标签组间的依赖关系。通过在MTG-Jamendo数据集上的音乐标签实验验证了该方法的有效性，并研究了链式顺序对标签性能的影响。
原文摘要 · Abstract (English)
We propose music tagging with classifier chains that model the interplay of music tags. Most conventional methods estimate multiple tags independently by treating them as multiple independent binary classification problems. This treatment overlooks the conditional dependencies among music tags, leading to suboptimal tagging performance. Unlike most music taggers, the proposed method sequentially estimates each tag based on the idea of the classifier chains. Beyond the naive classifier chains, the proposed method groups the multiple tags by category, such as genre, and performs chains by unit of groups, which we call \textit{classifier group chains}. Our method allows the modeling of the dependence between tag groups. We evaluate the effectiveness of the proposed method for music tagging performance through music tagging experiments using the MTG-Jamendo dataset. Furthermore, we investigate the effective order of chains for music tagging.
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