Using Automated Rhyme Detection to Characterize Rhyming Style in Rap Music

Abstract

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

MCFlow: A Digital Corpus of Rap Transcriptions

“…Rather, there seems to be an increase in rhyme density until 2002, followed by a reduction in the use of rhyme. This pattern is consistent with results found by Hirjee and Brown (2010). The dramatic increase in rhyme usage in the early 1990s may be associated with the transition between oldschool and new-school rap.…”

“…Linguist Jonah Katz (2008;2010) has presented more detailed linguistic analysis of rhyme in rap, including work which applies theories from music cognition. Finally, in computer science, Hirjee and Brown (2010) conducted a corpus analysis of the texts from several thousand rap songs by twenty-five popular artists. Hirjee and Brown noted a marked increase in the complexity of rhyme patterns found in rap between 1980 and 2000.…”

“…These projects have not considered the date of rap works as well, essentially regarding approaching flow as a static phenomenon. Only Hirjee and Brown's (2010) and, to a lesser extent Katz' (2008), work are based on significant samples of music, with only Hirjee and Brown specifically looking at historical trends. However, these linguistic analyses do not include any rhythmic information, approaching rap songs essentially as written texts.…”

Sampling and Features: A Commentary on Condit-Schultz (2016)

“…Holtman (1996) provides equivalence classes that group consonants of similar manner of articulation, but different place of articulation. Hirjee and Brown (2010) mitigate the issue by constructing a probabilistic model of rhyme similarity. But a good portion of the confusion is that emcees routinely alter the pronunciations of words in order to create greater phonetic similarity.…”

Style and Flow: A Commentary on Duinker & Martin

“…CORPUS-BASED methods in the study of hip-hop have proliferated over the past 10 to 15 years (Kawahara, 2007;Katz, 2008Katz, , 2015Hirjee & Brown, 2010;Horn, 2010); culminating in two hip-hop corpus studies being published in the same issue of this journal in 2016 (Condit-Schultz, 2016a;Ohriner, 2016a) along with associated commentaries (Gran, 2016;Condit-Schultz, 2016b;Ohriner, 2016b). A third study now joins them: Duinker & Martin's (2017) excellent examination of how musical, vocal, and production parameters factor into judgments of hip-hop 'style' or 'sound'.…”