The performance of music auto-tagging depends on the quality of training data. In practice, the links between songs and tags in the manually labeled training data can be incorrect (false positive) or missing (false negative). In this paper, we propose a cost-sensitive tag propagation learning method to improve auto-tagging. Specifically, we exploit music context to determine similar songs and propagate tags between them. Both propagated tags and original tags are used to optimize the auto-tagging models, and cost-sensitivity is incorporated into the loss function to enhance the robustness by adjusting the weight of relevant ( positive ) links with respect to irrelevant ( negative ) links. The proposed method is tested on three auto-tagging models: 2D-CNN, CRNN, and SampleCNN. The Million Song Dataset is used for training, and four music contexts, artist, playlist, tag, and listener, are used for song similarity measurement. The experimental results show 1) The proposed method can successfully improve the performance of the three auto-tagging models, 2) The cost-sensitive loss function helps reduce the impact of missing tags, and 3) The artist music context is more powerful for tag propagation than the other three music contexts.

中文翻译：

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用于音乐自动标记的标签传播和成本敏感学习

音乐自动标记的性能取决于训练数据的质量。在实践中，手动标记的训练数据中歌曲和标签之间的链接可能不正确（假阳性）或缺失（假阴性）。在本文中，我们提出了一种成本敏感的标签传播学习方法来改进自动标记。具体来说，我们利用音乐上下文来确定相似的歌曲并在它们之间传播标签。传播标签和原始标签都用于优化自动标签模型，并将成本敏感性纳入损失函数，通过调整相关的权重来增强鲁棒性（ 积极的 ) 与不相关 ( 消极的 ) 链接。在三种自动标记模型上测试了该方法：2D-CNN，CRNN和SampleCNN。使用百万歌曲数据集进行训练，使用艺术家、播放列表、标签和听众四个音乐上下文进行歌曲相似度测量。实验结果表明1）所提出的方法可以成功地提高三种自动标记模型的性能，2）成本敏感的损失函数有助于减少丢失标签的影响，以及3）艺术家音乐上下文对标签更强大传播比其他三个音乐背景。