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

Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin

Application of automatic speech recognition technologies to singing

The research field of Music Information Retrieval is concerned with the automatic analysis of musical characteristics. One aspect that has not received much attention so far is the automatic analysis of sung lyrics. On the other hand, the field of Automatic Speech Recognition has produced many methods for the automatic analysis of speech, but those have rarely been employed for singing. This thesis analyzes the feasibility of applying various speech recognition methods to singing, and suggests adaptations. In addition, the routes to practical applications for these systems are described. Five tasks are considered: Phoneme recognition, language identification, keyword spotting, lyrics-to-audio alignment, and retrieval of lyrics from sung queries. The main bottleneck in almost all of these tasks lies in the recognition of phonemes from sung audio. Conventional models trained on speech do not perform well when applied to singing. Training models on singing is difficult due to a lack of annotated data. This thesis offers two approaches for generating such data sets. For the first one, speech recordings are made more “song-like”. In the second approach, textual lyrics are automatically aligned to an existing singing data set. In both cases, these new data sets are then used for training new acoustic models, offering considerable improvements over models trained on speech. Building on these improved acoustic models, speech recognition algorithms for the individual tasks were adapted to singing by either improving their robustness to the differing characteristics of singing, or by exploiting the specific features of singing performances. Examples of improving robustness include the use of keyword-filler HMMs for keyword spotting, an i-vector approach for language identification, and a method for alignment and lyrics retrieval that allows highly varying durations. Features of singing are utilized in various ways: In an approach for language identification that is well-suited for long recordings; in a method for keyword spotting based on phoneme durations in singing; and in an algorithm for alignment and retrieval that exploits known phoneme confusions in singing.Das Gebiet des Music Information Retrieval befasst sich mit der automatischen Analyse von musikalischen Charakteristika. Ein Aspekt, der bisher kaum erforscht wurde, ist dabei der gesungene Text. Auf der anderen Seite werden in der automatischen Spracherkennung viele Methoden für die automatische Analyse von Sprache entwickelt, jedoch selten für Gesang. Die vorliegende Arbeit untersucht die Anwendung von Methoden aus der Spracherkennung auf Gesang und beschreibt mögliche Anpassungen. Zudem werden Wege zur praktischen Anwendung dieser Ansätze aufgezeigt. Fünf Themen werden dabei betrachtet: Phonemerkennung, Sprachenidentifikation, Schlagwortsuche, Text-zu-Gesangs-Alignment und Suche von Texten anhand von gesungenen Anfragen. Das größte Hindernis bei fast allen dieser Themen ist die Erkennung von Phonemen aus Gesangsaufnahmen. Herkömmliche, auf Sprache trainierte Modelle, bieten keine guten Ergebnisse für Gesang. Das Trainieren von Modellen auf Gesang ist schwierig, da kaum annotierte Daten verfügbar sind. Diese Arbeit zeigt zwei Ansätze auf, um solche Daten zu generieren. Für den ersten wurden Sprachaufnahmen künstlich gesangsähnlicher gemacht. Für den zweiten wurden Texte automatisch zu einem vorhandenen Gesangsdatensatz zugeordnet. Die neuen Datensätze wurden zum Trainieren neuer Modelle genutzt, welche deutliche Verbesserungen gegenüber sprachbasierten Modellen bieten. Auf diesen verbesserten akustischen Modellen aufbauend wurden Algorithmen aus der Spracherkennung für die verschiedenen Aufgaben angepasst, entweder durch das Verbessern der Robustheit gegenüber Gesangscharakteristika oder durch das Ausnutzen von hilfreichen Besonderheiten von Gesang. Beispiele für die verbesserte Robustheit sind der Einsatz von Keyword-Filler-HMMs für die Schlagwortsuche, ein i-Vector-Ansatz für die Sprachenidentifikation sowie eine Methode für das Alignment und die Textsuche, die stark schwankende Phonemdauern nicht bestraft. Die Besonderheiten von Gesang werden auf verschiedene Weisen genutzt: So z.B. in einem Ansatz für die Sprachenidentifikation, der lange Aufnahmen benötigt; in einer Methode für die Schlagwortsuche, die bekannte Phonemdauern in Gesang mit einbezieht; und in einem Algorithmus für das Alignment und die Textsuche, der bekannte Phonemkonfusionen verwertet

Music Encoding Conference Proceedings

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Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

The emergence of group stress in medieval French

The thesis investigates the development of the group-stress system in French from the earliest textual records to 1500. Empirical work is based on a corpus compiled especially for the study, which comprises 87 extracts from medieval French texts totalling over 250,000 words, composed mainly of verse texts to make use of the extra phonological information provided by the form. A unique metrical and syntactic annotation is used in the corpus to permit studies of phonological phrasing and stress placement in lines of verse. Much octosyllabic narrative verse, in particular texts associated with oral performance, is shown to have an iambic rhythmic tendency in the pre-1250 period, which is particularly strong in the earliest texts. No such effect is found in lyric texts or plays, or in narrative from after 1250. Additionally, a phonological phrase boundary is commonly found in the middle of the line. Iambic rhythmic organization is argued to be incompatible with group stress and associated ‘stress deafness’ effects observed in modern French. From this data, group stress is argued to have developed between the mid-12th and mid- 13th centuries. Work on modern French (e.g. Post, 2000) suggests that the stress group is the phonological phrase. Through reconstruction of the phonological phrasing of medieval French, the thesis demonstrates that regular word-final stress, the phonological phrase internal process of stress clash resolution, and the frequency of monosyllabic words combine to favour reanalysis of the French stress system in the pre-1250 period. Finally, the hypothesis that prosodic change affected verb-second word order in medieval French is reconsidered. It is argued that light clause-initial constituents which do not form their own phonological phrase (i.e. short adverbs, subject pronouns) become unstressed, a development which triggers syntactic changes that lead to the introduction of non verb-second word orders

The birth of 'modern' vocalism: The paradigmatic case of Enrico Caruso

In the decades spanning the turn of the twentieth century Italian opera singing underwent a profound transformation and became ‘modern’. I explore the formative elements of this modernity and its long-term effects on the way we sing today through the paradigmatic case of the tenor Enrico Caruso. I frame Caruso’s vocal evolution within the rise of verismo opera, comparing selected recordings, reviews and the rules and aesthetic prescriptions contained in vocal treatises to show how his new vocalism differed from that of the old bel canto. To set Caruso’s achievement in context I also analyse recordings of two other tenors of the era: Giovanni Zenatello and Alessandro Bonci

' "The Tale of the Tribe": The Twentieth-Century Alliterative Revival.'

This thesis studies the revival of Old English- and Norse-inspired alliterative versification in twentieth-century English poetry and poetics. It is organised as a chronological sequence of three case-studies: three authors, heirs to Romantic Nationalism, writing at twentieth-century intersections between Modernism, Postmodernism, and Medievalism. It indicates why this form attracted revival; which medieval models were emulated, with what success, in which modern works: the technique and mystique of alliterative verse as a modern mode. It differs from previous scholarship by advocating Kipling and Tolkien, by foregrounding the primacy of language, historical linguistics, especially the philological reconstruction of Germanic metre; and by, accordingly, methodological emphasis on formal scansion, taking account of audio recordings of Pound and Tolkien performing their poetry. It proposes the revived form as archaising, epic, mythopoeic, constructed by its exponents as an authentic poetic speech symbolising an archetypical Englishness—‘The Tale of the Tribe’. A trope emerges of revival of the culturally-‘buried’ native and innate, an ancestral lexico-metrical heritage conjured back to life. A substantial Introduction offers a primer of Old English metre and style: how it works, and what it means, according to Eduard Sievers’ (1850-1932) reconstruction. Chapter I promotes Rudyard Kipling (1865-1936) as pioneering alliterative poet, his engagement with Old-Northernism, runes, and retelling of the myth of Weland. Chapter II assesses the impact of Anglo-Saxon on and through Ezra Pound (1885-1972). Scansions of his ‘Seafarer’ and Cantos testify to the influence of Saxonising versification in the development of Pound’s Modernist language and free verse. Chapter III exhibits the alliterative oeuvre of J. R. R. Tolkien (1892-1973), featuring close readings of verse from Lord of the Rings. The Conclusion contends that twentieth-century English poetry should be recognised as evincing an ambitious alliterative revival, impossible before, and that this ancient metre is likely to endure into the future

The Copying and Collection of Music in the Trouvère Chansonnier F-Pn fr. 24406

F-Pn fr. 24406 is a codex of 155 folios containing, along with two Old-French prose works and a series of religious lyrics, 301 vernacular songs, all but one with notation. Despite its rich contents, fr. 24406 rarely receives mention in lists of the most important trouvère chansonniers. The majority of its contents are held in common with several of the other twenty chansonniers with notation. Editors consistently prefer other manuscripts’ musical and textual readings to those of fr. 24406, because of the uniqueness of its readings and its supposed inaccuracy. In this thesis, I argue that modern editorial principles have biased scholarship against perceiving what fr. 24406 has to offer and that both its history and its contents are to be valued. Its music scribes, by their very individuality and even their mistakes, reveal much about the notated transmission of the songs, about the previous existence of now-lost sources, and about the craftsmanship displayed by notators of vernacular monophony. I propose to view what has been seen as sloppiness in fr. 24406 as its flexibility. The medieval songbook, sometimes seen as an obstacle to scholarly access to authored originals or to medieval performances, is treated as in itself a worthy work of art. The thesis traces the themes of flexibility and scribal intelligence through the history of fr. 24406. My point of reference is the moment of copying and thus the thesis divides naturally into three parts: before, during and after copying. I begin after, with the combination shelf-mark’s two component manuscripts. The question of their relationship offers an occasion to trace the book’s usage since its compilation and changing scholarly opinions since its first notice. The task of manuscript description is thus largely accomplished through the lens of secondary scholarship. For the manuscript’s prehistory, I mine codicological evidence and apply musical comparison to demonstrate the existence of multiple lost, notated exemplars for fr. 24406. The final part of the thesis is then devoted to describing the notators against this backdrop. Comparison of notational techniques between sources lets us pinpoint the decisions of fr. 24406’s notators and describe their adaptability, their intelligence, and their craft.This PhD was funded by the Cambridge International Trus