Mathematical Notation, Representation, and Visualization of Musical Rhythm: A ComparativePerspective

Liu, Yang; Toussaint, Godfried T.

doi:10.7763/ijmlc.2012.v2.127

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…Their approach is limited to algorithmic concepts without showing visualizations that provide understanding for the analyst. Liu and Toussaint follow an approach to visualize rhythm using clock diagrams with a polygon notation to emphasize different rhythmic patterns [LT12]. Inspired by their approach, we create a radial design based on the clock metaphor to visualize the rhythmic content of symbolic music based on measure units (see Section 5.1).…”

Section: Background and Related Workmentioning

confidence: 99%

Augmenting Digital Sheet Music through Visual Analytics

Miller

Fürst

Schäfer

et al. 2022

Computer Graphics Forum

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Music analysis tasks, such as structure identification and modulation detection, are tedious when performed manually due to the complexity of the common music notation (CMN). Fully automated analysis instead misses human intuition about relevance. Existing approaches use abstract data‐driven visualizations to assist music analysis but lack a suitable connection to the CMN. Therefore, music analysts often prefer to remain in their familiar context. Our approach enhances the traditional analysis workflow by complementing CMN with interactive visualization entities as minimally intrusive augmentations. Gradual step‐wise transitions empower analysts to retrace and comprehend the relationship between the CMN and abstract data representations. We leverage glyph‐based visualizations for harmony, rhythm and melody to demonstrate our technique's applicability. Design‐driven visual query filters enable analysts to investigate statistical and semantic patterns on various abstraction levels. We conducted pair analytics sessions with 16 participants of different proficiency levels to gather qualitative feedback about the intuitiveness, traceability and understandability of our approach. The results show that MusicVis supports music analysts in getting new insights about feature characteristics while increasing their engagement and willingness to explore.

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Section: Background and Related Workmentioning

confidence: 99%

Augmenting Digital Sheet Music through Visual Analytics

Miller

Fürst

Schäfer

et al. 2022

Computer Graphics Forum

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“…Typically, readers of music perform close reading using the CMN that encodes all rhythmic details [32]. For instance, the binary string as a rudimentary visual representation can aid the analysis of rhythm by considering the presence or absence of sound [27]. This idea has been improved by the Time Unit Box System [45] and its generalization, Drum Tablature [39].…”

Section: Related Workmentioning

confidence: 99%

“…Another approach by Robledo exploits texture in the background of the CMN, which is highlighting rhythmic units [37] to improve their separation. These concepts use a linear sequence to communicate the rhythmic aspects visually but do not support the cyclic nature of rhythmic patterns [39] or their symmetry [27]. Circular representations are more suitable to address these two characteristics [27,39].…”

Section: Related Workmentioning

confidence: 99%

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Augmenting Sheet Music with Rhythmic Fingerprints

Fürst¹,

Miller²,

Keim³

et al. 2020

Preprint

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“…4.2(a) é [0 1 1 1], o que indica que a célula rítmica é composta de 4 figuras, sendo a primeira destas um silêncio, e as três restantes figuras de igual duração. Algumas das desvantagens dessa notação são a dificuldade no processamento de ritmos por computador (Liu & Toussaint, 2012), e que por só considerar o início dos eventos e não a duração, produz confusão com outros conjuntos de figuras rítmicas, por exemplo, ?ˇ"ˇ" ( compartilha o mesmo vetor binário do anterior exemplo.…”

Section: Representação E Codificação De Células Rítmicasunclassified

Mineração de estruturas musicais e composição automática utilizando redes complexas

Salazar¹

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Meu profundo agradecimento ao meu orientador o Prof. Dr. Zhao Liang pelas recomendações, paciência e ajudas prestadas durante o desenvolvimento da tese; à agência de fomento CAPES 1 pelo apoio financeiro recebido e a Daiana Ellen Canato por todo o amor, carinho, apoio e compreensão. Um especial agradecimento ao Prof. Dr. Emmanuel Vincent e Prof. Dr. Frédéric Bimbot do grupo de pesquisa METISS do INRIA da Universidade de Rennes 1 na França, pelo convite, parceria, e colaboração durante o meu estágio de pesquisa nessa universidade; e à Sra. Mariannick D. Laurent, pelo recebimento, atenção, orientação e amizade durante minha estadia em Rennes. Agradeço também ao pessoal do laboratório de Computação Bioinspirada (BioCom), especialmente a Bilzã Araújo, Filipi Neto Verri, Paulo Andretta Jaskowiak, Danilo Horta, Thiago Cupertino e Lilian Berton, pela resolução de dúvidas e colaborações eventuais; e à Prof.ª Dr.ª Roseli A. Francelin Romero, que me motivou a publicar os primeiros resultados desta tese e à minha colega Dr.ª Débora Cristina Corrêa por alguns dos tópicos que inspiraram esta pesquisa e pelo trabalho em coautoria; por fim, também agradeço a Julio César Herrera (Don Julio),

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Mathematical Notation, Representation, and Visualization of Musical Rhythm: A ComparativePerspective

Abstract: Abstract-Several methods for the mathematical notation, representation, and visualization of musical rhythm at the symbolic level are illustrated and compared in terms of their advantages and drawbacks, as well as their suitability for particular applications.

Cited by 8 publications

References 35 publications

Augmenting Digital Sheet Music through Visual Analytics

Augmenting Digital Sheet Music through Visual Analytics

Augmenting Sheet Music with Rhythmic Fingerprints

Mineração de estruturas musicais e composição automática utilizando redes complexas

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