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 real- world descriptions of rhyming style and were strongly characteristic of different rappers, resulting in potential applications to style-based comparison, music recommendation, and authorship identification.

Music listeners often mishear the lyrics to unfamiliar songs heard from public sources, such as the radio. Since standard text search engines will find few relevant results when they are entered as a query, these misheard lyrics require phonetic pattern matching techniques to identify the song. We introduce a probabilistic model of mishearing trained on examples of actual misheard lyrics, and develop a phoneme similarity scoring matrix based on this model. We compare this scoring method to simpler pattern matching algorithms on the task of finding the correct lyric from a collection given a misheard query. The probabilistic method significantly outperforms all other methods, finding 5-8% more correct lyrics within the first five hits than the previous best method.

Imperfect and internal rhymes are two important features in rap music often ignored in the music information retrieval community. We develop a method of scoring potential rhymes using a probabilistic model based on phoneme frequencies in rap lyrics. We use this scoring scheme to automatically identify internal and line-final rhymes in song lyrics and demonstrate the performance of this method compared to rules-based models. Higher level rhyme features are produced and used to compare rhyming styles in song lyrics from different genres, and for different rap artists.