Modelling Emotion Dynamics in Song Lyrics with State Space Models
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 104 references that are in the map. First posted on arXiv 17 Oct 2022.
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28 of 104 references.
References · 28
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16 more
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