Towards sufficient music searching, it is vital to form a complete set of labels for each song. However, current solutions fail to resolve it as they cannot produce diverse enough mappings to make up for the information missed by the gold labels. Based on the observation that such missing information may already be presented in user comments, we propose to study the automated music labeling in an essential but under-explored setting, where the model is required to harvest more diverse and valid labels from the users' comments given limited gold labels. To this end, we design an iterative framework (DiVa) to harvest more $\underline{\text{Di}}$verse and $\underline{\text{Va}}$lid labels from user comments for music. The framework makes a classifier able to form complete sets of labels for songs via pseudo-labels inferred from pre-trained classifiers and a novel joint score function. The experiment on a densely annotated testing set reveals the superiority of the Diva over state-of-the-art solutions in producing more diverse labels missed by the gold labels. We hope our work can inspire future research on automated music labeling.

翻译：为了充分满足音乐搜索需求，为每首歌曲建立完整的标签集至关重要。然而，现有解决方案未能解决这一问题，因为它们无法生成足够多样化的映射来补充黄金标签遗漏的信息。基于此类遗漏信息可能已存在于用户评论中的观察，我们提出在一个重要但尚未充分探索的场景下研究自动化音乐标注：要求模型在给定有限黄金标签的情况下，从用户评论中挖掘更多样化且有效的标签。为此，我们设计了一个迭代框架（DiVa），用于从用户评论中挖掘更$\underline{\text{多}}$样化且$\underline{\text{有}}$效的音乐标签。该框架通过从预训练分类器推断的伪标签以及新型联合评分函数，使分类器能够为歌曲形成完整的标签集。在密集标注测试集上的实验表明，DiVa在生成黄金标签遗漏的更多样化标签方面优于现有最优方案。我们希望本研究能启发未来关于自动化音乐标注的探索。