“Butter lyrics over hominy grit”†
Comparing audio and psychology-based text features in MIR tasks
Jaehun Kim (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Andrew M. Demetriou (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Sandy Manolios (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M. Stella Tavella (Musixmatch)
Cynthia C.S. Liem (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Psychology research has shown that song lyrics are a rich source of data, yet they are often overlooked in the field of MIR compared to audio. In this paper, we provide an initial assessment of the usefulness of features drawn from lyrics for various fields, such as MIR and Music Psychology. To do so, we assess the performance of lyric-based text features on 3 MIR tasks, in comparison to audio features. Specifically, we draw sets of text features from the field of Natural Language Processing and Psychology. Further, we estimate their effect on performance while statistically controlling for the effect of audio features, by using a hierarchical regression statistical model. Lyric-based features show a small but statistically significant effect, that anticipates further research. Implications and directions for future studies are discussed.
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