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What is this song about anyway?: Automatic classification of subject using user interpretations and lyrics

机译:这首歌到底是关于什么的?:使用用户解释和歌词对主题进行自动分类

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Metadata research for music digital libraries has traditionally focused on genre. Despite its potential for improving the ability of users to better search and browse music collections, music subject metadata is an unexplored area. The objective of this study is to expand the scope of music metadata research, in particular, by exploring music subject classification based on user interpretations of music. Furthermore, we compare this previously unexplored form of user data to lyrics at subject prediction tasks. In our experiment, we use datasets consisting of 900 songs annotated with user interpretations. To determine the significance of performance differences between the two sources, we applied Friedman's ANOVA test on the classification accuracies. The results show that user-generated interpretations are significantly more useful than lyrics as classification features (p < 0.05). The findings support the possibility of exploiting various existing sources for subject metadata enrichment in music digital libraries.
机译:音乐数字图书馆的元数据研究传统上侧重于类型。尽管它具有提高用户更好地搜索和浏览音乐收藏品的能力的潜力,但是音乐主题元数据仍然是一个未开发的领域。这项研究的目的是扩大音乐元数据的研究范围,尤其是通过探索基于用户对音乐的解释的音乐主题分类。此外,在主题预测任务中,我们将用户数据的这种以前未开发的形式与歌词进行了比较。在我们的实验中,我们使用由900首带有用户解释注释的歌曲组成的数据集。为了确定两种来源之间性能差异的重要性,我们对分类精度应用了Friedman的ANOVA检验。结果表明,用户生成的解释比歌词作为分类功能要有用得多(p <0.05)。这些发现支持了利用各种现有资源来丰富音乐数字图书馆中的主题元数据的可能性。

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