论文标题
用状态空间模型在歌曲歌词中建模情感动态
Modelling Emotion Dynamics in Song Lyrics with State Space Models
论文作者
论文摘要
音乐情感识别中的大多数作品都为整首歌都带有单首或几个歌曲级标签。尽管众所周知，不同的情绪在歌曲中的强度可能会有所不同，但此设置的注释数据很少且难以获得。在这项工作中，我们提出了一种方法，可以预测歌曲歌词中没有歌曲级监督的情感动态。我们将每首歌曲作为时间序列构图，并采用状态空间模型（SSM），将句子级的情感预测器与期望最大化（EM）程序相结合，以产生完整的情感动态。我们的实验表明，应用我们的方法始终如一地提高句子级基线的性能而无需任何带注释的歌曲，因此非常适合有限的培训数据情景。通过案例研究的进一步分析显示了我们方法的好处，同时还表明了局限性并指出了未来的方向。
Most previous work in music emotion recognition assumes a single or a few song-level labels for the whole song. While it is known that different emotions can vary in intensity within a song, annotated data for this setup is scarce and difficult to obtain. In this work, we propose a method to predict emotion dynamics in song lyrics without song-level supervision. We frame each song as a time series and employ a State Space Model (SSM), combining a sentence-level emotion predictor with an Expectation-Maximization (EM) procedure to generate the full emotion dynamics. Our experiments show that applying our method consistently improves the performance of sentence-level baselines without requiring any annotated songs, making it ideal for limited training data scenarios. Further analysis through case studies shows the benefits of our method while also indicating the limitations and pointing to future directions.
