Modelling Emotion Dynamics in Song Lyrics with State Space Models
This work addresses the challenge of modeling varying emotions in music for applications in music emotion recognition, though it is incremental as it builds on existing methods like State Space Models.
The authors tackled the problem of predicting emotion dynamics in song lyrics without song-level supervision, and their method consistently improved sentence-level baseline performance without requiring annotated songs.
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.