Using Automated Rhyme Detection to Characterize Rhyming Style in Rap Music

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

Rhyme, Rhythm, and Rhubarb: Using Probabilistic Methods to Analyze Hip Hop, Poetry, and Misheard Lyrics

While text Information Retrieval applications often focus on extracting semantic features to identify the topic of a document, and Music Information Research tends to deal with melodic, timbral or meta-tagged data of songs, useful information can be gained from surface-level features of musical texts as well. This is especially true for texts such as song lyrics and poetry, in which the sound and structure of the words is important. These types of lyrical verse usually contain regular and repetitive patterns, like the rhymes in rap lyrics or the meter in metrical poetry. The existence of such patterns is not always categorical, as there may be a degree to which they appear or apply in any sample of text. For example, rhymes in hip hop are often imperfect and vary in the degree to which their constituent parts differ. Although a definitive decision as to the existence of any such feature cannot always be made, large corpora of known examples can be used to train probabilistic models enumerating the likelihood of their appearance. In this thesis, we apply likelihood-based methods to identify and characterize patterns in lyrical verse. We use a probabilistic model of mishearing in music to resolve misheard lyric search queries. We then apply a probabilistic model of rhyme to detect imperfect and internal rhymes in rap lyrics and quantitatively characterize rappers' styles in their use. Finally, we compute likelihoods of prosodic stress in words to perform automated scansion of poetry and compare poets' usage of and adherence to meter. In these applications, we find that likelihood-based methods outperform simpler, rule-based models at finding and quantifying lyrical features in text

Lyrics Matter: Using Lyrics to Solve Music Information Retrieval Tasks

Music Information Retrieval (MIR) research tends to focus on audio features like melody and timbre of songs while largely ignoring lyrics. Lyrics and poetry adhere to a specific rhyme and meter structure which set them apart from prose. This structure could be exploited to obtain useful information, which can be used to solve Music Information Retrieval tasks. In this thesis we show the usefulness of lyrics in solving MIR tasks. For our first result, we show that the presence of lyrics has a variety of significant effects on how people perceive songs, though it is unable to significantly increase the agreement between Canadian and Chinese listeners about the mood of the song. We find that the mood assigned to a song is dependent on whether people listen to it, read the lyrics or both together. Our results suggests that music mood is so dependent on cultural and experiental context to make it difficult to claim it as a true concept. We also show that we can predict the genre of a document based on the adjective choices made by the authors. Using this approach, we show that adjectives more likely to be used in lyrics are more rhymable than those more likely to be used in poetry and are also able to successfully separate poetic lyricists like Bob Dylan from non-poetic lyricists like Bryan Adams. We then proceed to develop a hit song detection model using 31 rhyme, meter and syllable features and commonly used Machine Learning algorithms (Bayesian Network and SVM). We find that our lyrics features outperform audio features at separating hits and flops. Using the same features we can also detect songs which are likely to be shazamed heavily. Since most of the Shazam Hall of Fame songs are by upcoming artists, our advice to them is to write lyrically complicated songs with lots of complicated rhymes in order to rise above the "sonic wallpaper", get noticed and shazamed, and become famous. We argue that complex rhyme and meter is a detectable property of lyrics that indicates quality songmaking and artisanship and allows artists to become successful

In your eyes: identifying cliches in song lyrics

We investigated methods for the discovery of cliches from song lyrics. Trigrams and rhyme features were extracted from a collection of lyrics and ranked using term-weighting techniques such as tf-idf. These attributes were also examined over both time and genre. We present an application to produce a cliche score for lyrics based on these findings and show that number one hits are substantially more cliched than the average published song

A general framework for learning prosodic-enhanced representation of rap lyrics

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Learning and analyzing rap lyrics is a significant basis for many Web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features, are usually ignored. In this paper, we propose a hierarchical attention variational a utoe ncoder framework (HAVAE), which simultaneously considers semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in different rap lyrics learning tasks

Rhyme and Rhyming in Verbal Art, Language, and Song

This collection of thirteen chapters answers new questions about rhyme, with views from folklore, ethnopoetics, the history of literature, literary criticism and music criticism, psychology and linguistics. The book examines rhyme as practiced or as understood in English, Old English and Old Norse, German, Swedish, Norwegian, Finnish and Karelian, Estonian, Medieval Latin, Arabic, and the Central Australian language Kaytetye. Some authors examine written poetry, including modernist poetry, and others focus on various kinds of sung poetry, including rap, which now has a pioneering role in taking rhyme into new traditions. Some authors consider the relation of rhyme to other types of form, notably alliteration. An introductory chapter discusses approaches to rhyme, and ends with a list of languages whose literatures or song traditions are known to have rhyme

Rhyme and Rhyming in verbal Art, Language, and Song

This collection of thirteen chapters answers new questions about rhyme, with views from folklore, ethnopoetics, the history of literature, literary criticism and music criticism, psychology and linguistics. The book examines rhyme as practiced or as understood in English, Old English and Old Norse, German, Swedish, Norwegian, Finnish and Karelian, Estonian, Medieval Latin, Arabic, and the Central Australian language Kaytetye. Some authors examine written poetry, including modernist poetry, and others focus on various kinds of sung poetry, including rap, which now has a pioneering role in taking rhyme into new traditions. Some authors consider the relation of rhyme to other types of form, notably alliteration. An introductory chapter discusses approaches to rhyme, and ends with a list of languages whose literatures or song traditions are known to have rhyme.Peer reviewe