• Computational linguistics

    Abstract: In this paper, we discuss the interactive visualization project “How Does ‘Hamilton,’ the Non Stop, Hip-hop Broadway Sensation Tap Rap’s Master Rhymes to Blur Musical Lines?” and the rhyme-pattern analysis algorithm that made the piece possible. This interactive extracts and describes the rhyme patterns interlaced throughout Lin-Manuel Miranda’s Broadway hit “Hamilton,” analyzing their higher-order structure to produce a genealogy of rap influence in the popular musical. The final section of the interactive allows readers to input their own lyrics

    and view the algorithmic categorization of their submission’s rhymes. The project was created for the Wall Street Journal and published online. An adapted portion of the project also appeared in the print edition. This paper

    explains the motivations behind the project, and the research and development of the user-interaction models, a unique visual language for rhyme patterns and the algorithm that produces them. We then use this process to suggest methods for getting news readers to play with pop-cultural analysis as a tactic to create

    strong feedback loops of engagement with news interactives that are not specifically games.

     

    Keywords: Information visualization; audio; lyric analysis; rhyme schemes, rap; hip-hop; musical theater; pop culture; Hamilton

    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 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.

     

    Keywords: song lyrics, phonetic similarity, rhyme, hip hop, artist classification

     

    Abstract: This paper describes a new digital corpus of rap transcriptions known as the Musical Corpus of Flow (MCFlow). MCFlow currently contains transcriptions of verses from 124 popular rap songs, performed by 86 different rappers, containing a total of 374 verses, and consisting of 5,803 measures of music. MCFlow transcriptions contain rhythmic information, encoded in musical durations, as well as prosodic information, syntactic information, and phonetic information, including the identification of rhymes. In the second part of the paper, preliminary analyses of the corpus are presented, describing the "norms" of several important features of rap deliveries. These features include speed, rhyme density, metric position of stressed syllables, metric position of rhymes, phrase length, and the metric position of phrases. Several historical trends are identified, including an increase in rhyme density and phrase variability between 1980 and 2000. In each analysis, variance between different performers is compared to variance between songs. It is found that there is generally more variability between songs than between performers.

     

    Keywords: rap; corpus; rhythm; rhyme; phrasing; historical trends

     

    Abstract: Textsetting, the matching of linguistic objects and rhythmic ones, is a subject of enduring interest to researchers. Most studies of textsetting involve relatively simple forms such as children’s songs (Halle 1999), folk verse (Hayes & MacEachern 1996), and art verse similar to these traditions (Lerdahl 2001a). Hip-hop departs from many of the conventions of these genres, introducing additional levels of complexity. In this paper, I offer an analysis of hip-hop rhyme based on Lerdahl’s (2001b) analogy between rhyme and harmony in music. I argue that adopting the formalism of prolongational reduction from Lerdahl & Jackendoff’s (1983) seminal work A Generative Theory of Tonal Music (hencforth GTTM) allows us to analyze and actually predict many of the complex rhythmic phenomena encountered in modern hip-hop.

     

    Abstract: Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.

     

    Abstract:   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.

     

    Abstract:  In this paper, we focus on large scale poetry classification by meter. We repurposed an open source poetry

    scanning program (the Scandroid by Charles O. Hartman) as a feature extractor. Our machine learning experiments

    show a useful ability to classify poems by poetic meter. We also made our own rhyme detector using the Carnegie Melon University Pronouncing Dictionary as our primary source of pronunciation information. Future work will involve classifying rhyme and assembling a graph (or graphs) as part of the Graph Poem Project depicting the interconnected.

     

    Keywords: nlp; poetry; machine learning; decision trees; artificial intelligence; graph theory

     

    Abstract:  This dissertation describes the motivation, methodology, structure and content of a new symbolic corpus of rap vocal transcriptions known as the Musical Corpus of Flow (MCFlow). This corpus is intended to afford and inform research into the sonic organization of rapped vocals. An operational music theory of rap is presented, identifying the most artistically important features of rapped vocals and their most basic organizational structures. This theory informs and motivates the sampling and encoding scheme of MCFlow, which is described in detail. The content of the current MCFlow dataset is described as well: the current dataset includes transcriptions of 124 hip-hop songs by 47 artists, comprising 6,107 measures of music which contain 54,248 rapped words. Several preliminary descriptive analyses of the current dataset are presented as illustrations of MCFlow's usefulness for: (1) identifying normative structures in rap; (2) comparing the styles of different artists; (3) studying the historical evolution of rap artistry. Information regarding access to MCFlow data and tools for analyzing the data are presented and the MCFlow online Graphical User Interface usable by any user with no special software requirement sis described.

     

    The full dissertation is available here.

     

    Abstract: This paper describes an unsupervised, language-independent model for finding rhyme schemes in poetry, using no prior knowledge about rhyme or pronunciation.

     

    There's a more detailed presentation here

     

    Abstract: We employ statistical methods to analyze, generate, and translate rhythmic poetry. We first apply unsupervised learning to reveal word-stress patterns in a corpus of raw poetry. We then use these word-stress patterns, in addition to rhyme and discourse models, to generate English love poetry. Finally, we translate Italian poetry into English, choosing target realizations that conform to desired rhythmic patterns.

     

    Abstract:  We describe Hafez, a program that generates any number of distinct poems on a user supplied topic. Poems obey rhythmic and rhyme constraints. We describe the poetry generation algorithm, give experimental data concerning its parameters, and show its generality with respect to language and poetic form.

     

    Abstract: Identification functions of 20 initial and 20 final consonants were characterized in 9600 randomly sampled consonant-vowel-consonant CVC tokens presented in speech-spectrum noise. Because of differences in the response criteria for different consonants, signal detection measures were used to quantify identifiability. Consonant-specific baseline signal-to-noise ratios SNRs were adjusted to produce a d of 2.20 for each consonant. Consonant identification was measured at baseline SNRs B , at B−6, and at B+6 dB. Baseline SNRs varied by more than 40 dB for different consonants. Confusion analysis revealed that single-feature place-of-articulation errors predominated at the highest SNR, while combined-feature errors predominated at the lowest SNR. Most consonants were identified at lower SNRs in initial than final syllable position. Vowel nuclei /Ä /, /i/, or /u/ significantly influenced the identifiability of 85% of consonants, with consistent vowel effects seen for consonant classes defined by manner, voicing, and place. Manner and voicing of initial and final consonants were processed independently, but place cues interacted: initial and final consonants differing in place of articulation were identified more accurately than those sharing the same place. Consonant identification in CVCs reveals contextual complexities in consonant processing.

     

    Abstract:  Computational techniques for scoring essays have recently come into use. Their bases and development methods raise both old and new measurement issues. However, coming principally from computer and cognitive sciences, they have received little attention from the educational measurement community. We briefly survey the state of the technology, then describe one such system, the Intelligent Essay Assessor (IEA). IEA is based largely on Latent Semantic Analysis (LSA), a machine-learning model that induces the semantic similarity of words and passages by analysis of large bodies of domain-relevant text. IEA’s dominant variables are computed from comparisons with pre-scored essays of highly similar content as measured by LSA. Over many validation studies with a wide variety of topics and test-takers, IEA correlated with human graders as well as they correlated with each other. The technique also supports other educational applications. Critical measurement questions are posed and discussed.

     

    Abstract:  Tra-la-Lyrics is a system that generates song lyrics automatically. In its original version, the main focus was to produce text where stresses matched the rhythm of given melodies. There were no concerns on whether the text made sense or if the selected words shared some kind of semantic association. In this article, we describe the development of a new version of Tra-la-Lyrics, where text is generated on a semantic domain, defined by one or more seed words. This effort involved the integration of the original rhythm module of Tra-la-Lyrics in PoeTryMe, a generic platform that generates poetry with semantically coherent sentences. To measure our progress, the rhythm, the rhymes, and the semantic coherence in lyrics produced by the original Tra-la-Lyrics were analysed and compared with lyrics produced by the new instantiation of this system, dubbed Tra-la-Lyrics 2.0. The analysis showed that, in the lyrics by the new system, words have higher semantic association among them and with the given seeds, while the rhythm is still matched and rhymes are present. The previous analysis was complemented with a crowdsourced evaluation, where contributors answered a survey about relevant features of lyrics produced by the previous and

    the current versions of Tra-la-Lyrics. Though tight, the survey results confirmed the improvements of the lyrics by Tra-la-Lyrics 2.0.

     

    Keywords: computational creativity, linguistic creativity, song lyrics, poetry, rhythm, semantics

    Exploring the Music Genome: Lyric Clustering with Heterogeneous Features

     

    Abstract:   This research explores the clustering of songs using lyrics features grouped into similar classes and heterogeneous combinations. Simple techniques are used to extract 140 features for analysis with Kohonen self-organising maps. These maps are evaluated using visual analysis and objective measures of validity with respect to the clustering of eight hand-selected song pairs. According to gold standard human-authored playlists, judgments of song similarity are based strongly on music, however this observation may be limited to playlists and is not necessarily extensible to music in the wider domain. In particular, since test song pairs could only be effectively matched when they were from the same genre, analysis of the correspondence between lyrics and expert human judgments of genre and style may be more fruitful than comparison with similarities observed in playlists. Results suggest that for music in the hard-to-differentiate categories of pop, rock and related genres, a combination of features relating to language, grammar, sentiment and repetition improve on the clustering performance of Information Space with a more accurate analysis of song similarity and increased sensitivity to the nuances of

    song style. SOM analysis further suggests that a few well-chosen attributes may be as good as, if not better than, deep analysis using many features. Results using stress patterns are inconclusive. Although results are preliminary and need to be validated with further research on a larger data set, to the knowledge of this author this is the first time success has been reported in differentiating songs in the rock/pop genre.

     

    Abstract:   How individuals perceive music is influenced by many dif- ferent factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information influences how we experience music, as does the songs' text and its sound. Next to symbolic and audio based music information re- trieval, which focus on the sound of music, song lyrics, may thus be used to improve classification or similarity ranking of music. Song lyrics exhibit specific properties different from traditional text documents - many lyrics are for exam- ple composed in rhyming verses, and may have different fre- quencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use 'slang' language or differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word / verse length, and the amount of repetative text. In this paper, we present a novel set of features devel- oped for textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classifica- tion on a test collection in order to demonstrate our analysis.

     

    Abstract:  When someone wishes to find the lyrics for a song they typically go online and use a search engine. There are a large number of lyrics available on the internet as the effort required to transcribe and post lyrics is minimal.

    These lyrics are promptly returned to the user with customary search engine page ranking formula deciding the

    ordering of these results based on links, views, clicks, etc. However the content, and specifically, the accuracy of the
    lyrics in question are not analysed or used in any way to determine the rank of the lyrics, despite this being of con-
    cern to the searcher. In this work, we show that online lyrics are often inaccurate and the ranking methods used

    by search engines do not distinguish the more accurate annotations. We present an alternative method for ranking

    lyrics based purely on the collection of lyrics themselves using the Lyrics Concurrence.

     

    Abstract:  Retrieving the lyrics of a sung recording from a database of text documents is a research topic that has not received much attention so far. Such a retrieval system has many practical applications, e.g. for karaoke applications or for indexing large song databases by their lyric content. We present a new method for lyrics retrieval. An acoustic model trained on singing is used to obtain phoneme probabilities from sung

    queries, which are then mapped to phoneme sequences. These are compared against lines of textual lyrics in a large corpus in order to retrieve the best-matching song. The approach is tested on three sung datasets. Lyrics are

    retrieved from a set of 300 possible songs (12,000 lines of lyrics). The results are highly encouraging and could be used further to perform automatic lyrics alignment and keyword spotting for large databases of songs, or for retrieving lyrics from the internet.

     

    Keywords: Lyrics, Text retrieval, Singing, Automatic Speech Recognition, Music Information Retrieval

     

    Abstract:  Not all learning takes place in an educational setting: more and more self-motivated learners are turning to on-line text to learn about new topics. Our goal is to provide such learners with the well-known benefits of testing by automatically generating quiz questions for on-line text. Prior work on question generation has focused on the grammaticality of generated questions and generating effective multiple-choice distractors for individual question

    targets, both key parts of this problem. Our work focuses on the complementary aspect of determining what part of a sentence we should be asking about in the first place; we call this “gap selection.” We address this problem by
    asking human judges about the quality of questions generated from a Wikipedia-based corpus, and then training a model to effectively replicate these judgments. Our data shows that good gaps are of variable length and span
    all semantic roles, i.e., nouns as well as verbs, and that a majority of good questions do not focus on named entities. Our resulting system can generate fill-in-the-blank (cloze) questions from generic source materials.

     

    Executive summary: The new assessment systems designed to measure the Common Core standards will include far more performance-based items and tasks than most of today’s assessments. In order to provide feedback quickly and make scoring of the responses affordable, the new assessments will rely heavily on artificial intelligence scoring, hereafter referred to as automated scoring. Familiarity with the current range of applications and the operational accuracy of automatic scoring technology can help provide an understanding of which item types can be scored automatically in the near term. This document describes several examples of current item types that Pearson has designed and fielded successfully with automatic scoring. The item examples are presented along with operational reliability and accuracy figures, as well as information of the nature and development of the automated scoring systems used by Pearson.

     

    Abstract: WordNet is an on-line lexical reference system whose design is inspired by current psycholinguistic theories of human lexical memory. English nouns, verbs, and adjectives are organized into synonym sets, each representing one underlying lexical concept. Different relations link the synonym sets.

     

    Abstract: In the REAP system, users are automatically provided with texts to read targeted to their individual reading levels. To find appropriate texts, the user’s vocabulary knowledge must be assessed. We describe an approach to automatically generating questions for vocabulary assessment. Traditionally, these assessments have been hand-written. Using data from WordNet, we generate 6 types of vocabulary questions. They can have several forms, including wordbank and multiple-choice. We present experimental results that suggest that these automatically-generated questions give a measure of vocabulary skill that correlates well with subject perform-
    ance on independently developed human-written questions. In addition, strong correlations with standardized vocabulary tests point to the validity of our approach to automatic assessment of word knowledge.

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    A cookie is a small file which asks permission to be placed on your computer's hard drive. Once you agree, the file is added and the cookie helps analyse web traffic or lets you know when you visit a particular site. Cookies allow web applications to respond to you as an individual. The web application can tailor its operations to your needs, likes and dislikes by gathering and remembering information about your preferences.
    
    We use traffic log cookies to identify which pages are being used. This helps us analyse data about web page traffic and improve our website in order to tailor it to customer needs. We only use this information for statistical analysis purposes.
    
    Overall, cookies help us provide you with a better website, by enabling us to monitor which pages you find useful and which you do not. A cookie in no way gives us access to your computer or any information about you, other than the data you choose to share with us. You can choose to accept or decline cookies. Most web browsers automatically accept cookies, but you can usually modify your browser setting to decline cookies if you prefer. This may prevent you from taking full advantage of the website.
    
    Links to other websites
    
    Our website may contain links to other websites of interest. However, once you have used these links to leave our site, you should note that we do not have any control over that other website. Therefore, we cannot be responsible for the protection and privacy of any information which you provide whilst visiting such sites and such sites are not governed by this privacy statement. You should exercise caution and look at the privacy statement applicable to the website in question.
    
    Controlling your personal information
    
    We will not sell, distribute or lease your personal information to third parties unless we have your permission or are required by law to do so. We may use your personal information to send you promotional information about third parties but this will be on an individual basis and you will not receive mass mailouts from LYRICAL KOMBAT. We may email you to recommend an event-related service or idea, but it would be specific to you and your situation.
    
    You may request details of personal information which we hold about you under the Data Protection Act 1998. A small fee will be payable. If you would like a copy of the information held on you please write to.
    
    If you believe that any information we are holding on you is incorrect or incomplete, please write to or email us – hello[at]lyricalkombat We will promptly correct any information found to be incorrect.