Imperfect and internal rhymes are two important features in rap music previously ignored in the music information retrieval literature. We developed a method of scoring potential rhymes using a probabilistic model based on phoneme frequencies in rap lyrics. We used this scoring scheme to automatically identify internal and line-final rhymes in song lyrics and demonstrated the performance of this method compared to rules-based models. We then calculated higher-level rhyme features and used them to compare rhyming styles in song lyrics from different genres, and for different rap artists. We found that these detected features corresponded to realworld descriptions of rhyming style and were strongly characteristic of different rappers, resulting in potential applications to style-based comparison, music recommendation, and authorship identification. KEYWORDS: song lyrics, phonetic similarity, rhyme, hip hop, artist classificationSONG lyrics have received relatively little attention in music information retrieval, but can provide data about song style or content that is missing from raw audio files or user-input tags. Recent work focusing on lyrics (Fujihara, 2008;Kleedorfer, 2008;Wei, 2007) uses the meaning of lyric text words to extract song topic, theme, or mood information; the pattern and sound of the words themselves is usually ignored.These sound features are central to rap music, providing information about vocal delivery and rhyme scheme. These data can be characteristic of different rappers, as MCs often boast of the uniqueness and superiority of their rhyming style (Bradley, 2009). Lyric rhymes have previously been studied as an aid in predicting musical genres (Mayer, 2008), but this prior work ignores two stylistic features of rap lyrics: imperfect rhymes, where syllable end sounds are similar, but not identical; and internal rhyme, which occurs in the middle of lines.To study these features, we developed a system for automatic detection of rap music rhymes. We trained a probabilistic scoring model of rhymes using a corpus of rap lyrics known to be rhyming, using ideas derived from bioinformatics. We then used this model to find and categorize various rhymes in different song lyrics, and assessed the model's success. High-level statistical rhyme scheme features we calculated allowed us to quantitatively model and compare rhyming styles between artists and genres. These features correlated with real-world notions of rapping style and we identified trends in their use in hip hop music over time. Finally, we used these rhyme features to classify rappers and investigated potential applications of rhyme stylometry. This article is the expanded version of a conference paper (Hirjee, 2009) presented at ISMIR 2009