DopeLearning: A Computational Approach to Rap Lyrics Generation∗ Eric Malmi Pyry Takala Hannu Toivonen Aalto University and HIIT Aalto University University of Helsinki and HIIT Espoo, Finland Espoo, Finland Helsinki, Finland eric.malmi@aalto.fi pyry.takala@aalto.fi
[email protected].fi Tapani Raiko Aristides Gionis Aalto University Aalto University and HIIT Espoo, Finland Espoo, Finland tapani.raiko@aalto.fi aristides.gionis@aalto.fi ABSTRACT work. Second, with the number of smart devices increasing Writing rap lyrics requires both creativity to construct a that we use on a daily basis, it is expected that the demand meaningful, interesting story and lyrical skills to produce will increase for systems that interact with humans in non- complex rhyme patterns, which form the cornerstone of good mechanical and pleasant ways. flow. We present a rap lyrics generation method that cap- Rap is distinguished from other music genres by the formal tures both of these aspects. First, we develop a prediction structure present in rap lyrics, which makes the lyrics rhyme model to identify the next line of existing lyrics from a set of well and hence provides better flow to the music. Literature candidate next lines. This model is based on two machine- professor Adam Bradley compares rap with popular music learning techniques: the RankSVM algorithm and a deep and traditional poetry, stating that while popular lyrics lack neural network model with a novel structure. Results show much of the formal structure of literary verse, rap crafts that the prediction model can identify the true next line \intricate structures of sound and rhyme, creating some of among 299 randomly selected lines with an accuracy of 17%, the most scrupulously formal poetry composed today" [5].