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Most previous work in music emotion recognition assumes a single or a fewsong-level labels for the whole song. While it is known that different emotionscan vary in intensity within a song, annotated data for this setup is scarceand difficult to obtain. In this work, we propose a method to predict emotiondynamics in song lyrics without song-level supervision. We frame each song as atime series and employ a State Space Model (SSM), combining a sentence-levelemotion predictor with an Expectation-Maximization (EM) procedure to generatethe full emotion dynamics. Our experiments show that applying our methodconsistently improves the performance of sentence-level baselines withoutrequiring any annotated songs, making it ideal for limited training datascenarios. Further analysis through case studies shows the benefits of ourmethod while also indicating the limitations and pointing to future directions.

Modelling Emotion Dynamics in Song Lyrics with State Space Models