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  2020 (22)
Implementação de um sistema composicional semiaberto a partir da similaridade entre conjuntos de classes de notas. Braga, V.; Penchel, J.; Chagas, I.; Furman, R.; Proença, P.; and Pitombeira, L. OPUS, 26(2): 1. oct 2020.
Implementação de um sistema composicional semiaberto a partir da similaridade entre conjuntos de classes de notas [link]Paper   doi   bibtex   abstract   2 downloads
Digital Approaches to Troubadour Song. Chapman, K. E. Ph.D. Thesis, Indiana University, 2020.
Digital Approaches to Troubadour Song [link]Paper   bibtex   abstract   1 download
Mode classification and natural units in plainchant. Cornelissen, B.; Zuidema, W.; and Burgoyne, J. A. In Proceedings of 21th International Society for Music Information Retrieval Conference, Montréal, Canada, 2020.
Mode classification and natural units in plainchant [link]Paper   bibtex
Studying Large Plainchant Corpora Using chant21. Cornelissen, B.; Zuidema, W.; and Burgoyne, J. A. In Proceedings of 7th International Conference on Digital Libraries for Musicology, Montréal, Canada, 2020.
Studying Large Plainchant Corpora Using chant21 [link]Paper   bibtex
Using Note-Level Music Encodings to Facilitate Interdisciplinary Research on Human Engagement with Music. Devaney, J. Transactions of the International Society for Music Information Retrieval, 3(1): 205–217. oct 2020.
Using Note-Level Music Encodings to Facilitate Interdisciplinary Research on Human Engagement with Music [link]Paper   doi   bibtex   abstract
Voice-Leading Schema Recognition Using Rhythm and Pitch Features. Finkensiep, C.; Déguernel, K.; Neuwirth, M.; and Rohrmeier, M. In Proceedings of 21st International Conference on Music Information Retrieval, pages 520–526, Montréal, Canada, 2020.
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Miles Vs. Trane: Computational and Statistical Comparison of the Improvisatory Styles of Miles Davis and John Coltrane. Frieler, K. Jazz Perspectives, 12(1): 123–145. jan 2020.
Miles Vs. Trane: Computational and Statistical Comparison of the Improvisatory Styles of Miles Davis and John Coltrane [link]Paper   doi   bibtex   abstract   2 downloads
Reprise Structures in Haydn's Op. 50 Minuets. Inman, S. M. Indiana Theory Review, 36(1-2): 23–55. 2020.
Reprise Structures in Haydn's Op. 50 Minuets [link]Paper   bibtex   1 download
Music genre descriptor for classification based on tonnetz trajectories. Karystinaios, E.; Guichaoua, C.; Andreatta, M.; Bigo, L.; and Bloch, I. Journées d'Informatique Musicale. 2020.
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A Survey on Visualizations for Musical Data. Khulusi, R.; Kusnick, J.; Meinecke, C.; Gillmann, C.; Focht, J.; and Jänicke, S. Computer Graphics Forum, 00(00): 1–28. 2020.
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A corpus-based analysis of syncopated patterns in Ragtime. Kirlin, P. B In Proceedings of 21th International Society for Music Information Retrieval Conference, Montréal, Canada, 2020.
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Rule mining for local boundary detection in melodies. van Kranenburg, P. In Proceedings of 21st International Conference on Music Information Retrieval, pages 271–278, Montréal, Canada, 2020.
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Historiography of the form of symbolic music through a computer-assisted analysis. Kutschke, B. R.; and Bachmann, T. In Proceedings of the 17th Sound and Music Computing Conference, pages 386–393, Torino, 2020.
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Grouping compositions based on similarity of music themes. Laskowska, B.; and Kamola, M. PLoS ONE, 15(10). oct 2020.
Grouping compositions based on similarity of music themes [link]Paper   doi   bibtex
Not All Roads Lead to Rome: Pitch Representation and Model Architecture for Automatic Harmonic Analysis. Micchi, G.; Gotham, M.; and Giraud, M. Transactions of the International Society for Music Information Retrieval, 3(1): 42–54. 2020.
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A Systematic Literature Review on Computational Musicology. Mor, B.; Garhwal, S.; and Kumar, A. Archives of Computational Methods in Engineering, 27(3): 923–937. 2020.
A Systematic Literature Review on Computational Musicology [link]Paper   doi   bibtex   abstract
Harmony and Form in Brazilian Choro: A Corpus-Driven Approach to Musical Style Analysis. Moss, F. C.; de Souza, W. F.; and Rohrmeier, M. Journal of New Music Research, 0(0): 1–22. 2020.
Harmony and Form in Brazilian Choro: A Corpus-Driven Approach to Musical Style Analysis [link]Paper   doi   bibtex   1 download
SymPlot : A Web-Tool to Visualise Symbolic Musical Data. Muñoz-Lago, P.; Llorens, A.; Parada-Cabaleiro, E.; and Torrente, Á. In Proc. 24th International Converence Information Visualisation (IV), pages 515–521, Melbourne, Australia, 2020.
SymPlot : A Web-Tool to Visualise Symbolic Musical Data [pdf]Paper   doi   bibtex   2 downloads
Mining Characteristic Patterns for Comparative Music Corpus Analysis. Neubarth, K.; and Conklin, D. Applied Sciences, 10(6). 2020.
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From Music Ontology towards Ethno-Music-Ontology. Proutskova, P.; Volk, A.; Fazekas, G.; and Heidarian, P. In Proceedings of 21st International Conference on Music Information Retrieval, pages 923–931, Montréal, Canada, 2020.
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A quantitative study of pitch registers in string quartets opus 17, by Joseph Haydn. Sampaio, M. d. S.; de Oliveira, V. S.; Travassos, M.; and Castro, C. Musica Theorica, 5(1): 119–177. 2020.
A quantitative study of pitch registers in string quartets opus 17, by Joseph Haydn [link]Paper   bibtex   abstract   1 download
A cluster analysis of harmony in the McGill Billboard dataset. Shaffer, K.; Vasiete, E.; Jacquez, B.; Davis, A.; Escalante, D.; Hicks, C.; McCann, J.; Noufi, C.; and Salminen, P. Empirical Musicology Review, 14(3-4): 146. jul 2020.
A cluster analysis of harmony in the McGill Billboard dataset [link]Paper   doi   bibtex   abstract
  2019 (24)
Learning Sonata Form Structure on Mozart's String Quartets. Allegraud, P.; Bigo, L.; Feisthauer, L.; Giraud, M.; Groult, R.; Leguy, E.; and Levé, F. Transactions of the International Society for Music Information Retrieval, 2(1): 82–96. 2019.
Learning Sonata Form Structure on Mozart's String Quartets [link]Paper   doi   bibtex
A Computational Model of Immanent Accent Salience in Tonal Music. Bisesi, E.; Friberg, A.; and Parncutt, R. Frontiers in Psychology, 10(MAR): 1–19. mar 2019.
A Computational Model of Immanent Accent Salience in Tonal Music [link]Paper   doi   bibtex   abstract
Alternative measures: A musicologist workbench for popular music. Clark, B.; and Arthur, C. In Proceedings of the Sound and Music Computing Conferences, pages 407–414, 2019.
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HUMDRUMR : a new take on an old approach to Computational Musicology. Condit-Schultz, N.; and Arthur, C. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 715–722, Delft, Netherlands, 2019.
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Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval. Crayencour, H.; and Cella, C. Frontiers in Digital Humanities, 6(April): 1–25. 2019.
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Modeling and learning structural breaks in sonata forms. Feisthauer, L.; Bigo, L.; and Giraud, M. In Proc. International Society for Music Information Retrieval Conference 2019, Utrecht, Netherlands, 2019.
Modeling and learning structural breaks in sonata forms [link]Paper   bibtex   abstract
Visualizing music similarity: clustering and mapping 500 classical music composers. Georges, P.; and Nguyen, N. Scientometrics, (0123456789). 2019.
Visualizing music similarity: clustering and mapping 500 classical music composers [link]Paper   doi   bibtex
Taking Form: a representation standard, conversion code, and example corpus for recording, visualizing, and studying analysis of musical form. Gotham, M.; and Ireland, M. T In Proceedings of the 20th International Society for Music Information Retrieval Conference, Delft, Netherlands, 2019.
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Minor-Mode Sonata-Form Dynamics in Haydn's String Quartets. Hall, M. J. Haydn: Online Journal of the Haydn Society of North America, 9(1). 2019.
Minor-Mode Sonata-Form Dynamics in Haydn's String Quartets [pdf]Paper   bibtex   abstract
Algorithmic Ability to Predict the Musical Future: Datasets and Evaluation. Janssen, B.; Collins, T.; and Ren, I. Y. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 208–215, Delft, Netherlands, 2019.
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Annotator subjectivity in harmony annotations of popular music. Koops, H. V.; de Haas, W. B.; Burgoyne, J. A.; Bransen, J.; Kent-Muller, A.; and Volk, A. Journal of New Music Research, 0(0): 1–21. 2019.
Annotator subjectivity in harmony annotations of popular music [link]Paper   doi   bibtex
A Deep Learning Approach to generate Beethoven's 10th Symphony. Lago, P. M. Ph.D. Thesis, Universidad Complutense de Madrid, 2019.
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What Constitutes a Musical Pattern ?. Melkonian, O.; Ren, I. Y.; Swierstra, W.; and Volk, A. In Proceedings of the 7th ACM SIGPLAN International Workshop on Functional Art, Music, Modeling, and Design (FARM '19), Berlin, 2019.
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Statistical characteristics of tonal harmony: a corpus study of Beethoven's string quartets. Moss, F. C.; Neuwirth, M.; Harasim, D.; and Rohrmeier, M. PLoS ONE,1–16. 2019.
Statistical characteristics of tonal harmony: a corpus study of Beethoven's string quartets [link]Paper   doi   bibtex
Transitions of Tonality: A Model-Based Corpus Study. Moss, F. C. Ph.D. Thesis, École Polytehcnique Fédérale de Lausanne, 2019.
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Contributing to New Musicological Theories with Computational Methods: The Case of Centonization in Arab-Andalusian Music. Nuttall, T.; García-Casado, M.; Núñez-Tarifa, V.; Repetto, R. C.; and Serra, X. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 223–228, Delft, Netherlands, 2019.
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Towards a Graphical User Interface for Quantitative Analysis in Digital Musicology. Ortloff, A.; Güntner, L.; and Schmidt, T. In Proc. Mensch und Computer 2019 - Workshopband, pages 535–538, Hamburg, Germany, 2019. Gesellschaft für Informatik e.V.
Towards a Graphical User Interface for Quantitative Analysis in Digital Musicology [link]Paper   doi   bibtex   abstract
Evolution of the Informational Complexity of Contemporary Western Music. Parmer, T.; and Ahn, Y. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 175–182, Delft, The Netherlands, 2019.
Evolution of the Informational Complexity of Contemporary Western Music [link]Paper   doi   bibtex   abstract
A convolutional approach to melody line identification in symbolic scores. Simonetta, F.; Cancino-chacón, C.; Widmer, G.; and Ntalampiras, S. Computing Research Repository, abs/1906.1. 2019.
A convolutional approach to melody line identification in symbolic scores [pdf]Paper   bibtex
Machine learning research that matters for music creation: A case study. Sturm, B. L.; Ben-Tal, O.; Monaghan, Ú.; Collins, N.; Herremans, D.; Chew, E.; Hadjeres, G.; Deruty, E.; and Pachet, F. Journal of New Music Research, 48(1): 36–55. jan 2019.
Machine learning research that matters for music creation: A case study [link]Paper   doi   bibtex   abstract
Second-Position Syncopation in European and American Vocal Music. Temperley, D. Empirical Musicology Review, 14(1-2): 66. nov 2019.
Second-Position Syncopation in European and American Vocal Music [link]Paper   doi   bibtex   abstract
The RomanText Format: A Flexible and Standard Method for Representing Roman Numerial Analyses. Tymoczko, D.; Gotham, M.; Cuthbert, M.; and Ariza, C. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 123–129, Delft, The Netherlands, 2019.
The RomanText Format: A Flexible and Standard Method for Representing Roman Numerial Analyses [link]Paper   doi   bibtex
Tests of contrasting expressive content between first and second musical themes. Warrenburg, L. A; and Huron, D. Journal of New Music Research, 48(1): 21–35. 2019.
Tests of contrasting expressive content between first and second musical themes [link]Paper   doi   bibtex   abstract
Distinguishing Chinese Guqin and Western Baroque pieces based on statistical model. Wu, Y.; and Li, S. In Proceedings of Computer Music Multidisciplinary Research 2019, pages 1–12, 2019.
Distinguishing Chinese Guqin and Western Baroque pieces based on statistical model [link]Paper   bibtex
  2018 (15)
Relevance of musical features for cadence detection. Bigo, L.; Feisthauer, L.; Giraud, M.; and Levé, F. In Proceedings of 19th International Conference on Music Information Retrieval, Paris, 2018.
Relevance of musical features for cadence detection [link]Paper   bibtex   abstract
An Exploratory Study of Western Orchestration: Patterns through History. Chon, S. H.; Huron, D.; and DeVlieger, D. Empirical Musicology Review, 12(3-4): 116. jun 2018.
An Exploratory Study of Western Orchestration: Patterns through History [link]Paper   doi   bibtex   abstract
An overview of emerging pattern mining in supervised descriptive rule discovery: taxonomy, empirical study, trends, and prospects. García-Vico, A. M.; Carmona, C. J.; Martín, D.; García-Borroto, M.; and del Jesus, M. J. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1): 1–22. 2018.
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Dezrann, a Web Framework to Share Music Analysis. Giraud, M.; Groult, R.; and Leguy, E. In Proceedings of the International Conference on Technologies for Music Notation and Representation, pages 104–110, Montréal, Canada, 2018.
Dezrann, a Web Framework to Share Music Analysis [link]Paper   bibtex   abstract
Where Does Haydn End and Mozart Begin? Composer Classification of String Quartets. Kempfert, K. C.; and Wong, S. W. K. sep 2018.
Where Does Haydn End and Mozart Begin? Composer Classification of String Quartets [link]Paper   bibtex   abstract
X Section and Ethos in Sonata Forms by Haydn, Mozart, and early Beethoven. Libin, D. M Ph.D. Thesis, The State University of New Jersey, 2018.
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Another Look at Chromatic Third-Related Key Relationships in Late Haydn: Parallel Keys and Remote Modulation in Selected String Quartet Minuets. MacKay, J. S. Haydn: Online Journal of the Haydn Society of North America, 2(3): 1–27. 2018.
Another Look at Chromatic Third-Related Key Relationships in Late Haydn: Parallel Keys and Remote Modulation in Selected String Quartet Minuets [link]Paper   bibtex   abstract
Interesting Haydn: On Attention's Materials. Mathew, N. Journal of the American Musicological Society, 71(3): 655–701. 2018.
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The Annotated Beethoven Corpus (ABC): A Dataset of Harmonic Analyses of All Beethoven String Quartets. Neuwirth, M.; Harasim, D.; Moss, F. C.; and Rohrmeier, M. Frontiers in Digital Humanities, 5(July): 1–5. 2018.
The Annotated Beethoven Corpus (ABC): A Dataset of Harmonic Analyses of All Beethoven String Quartets [link]Paper   doi   bibtex
Contour Similarity Algorithms. Sampaio, M. d. S. MusMat - Brazilian Journal of Music and Mathematics, 2(2): 58–78. 2018.
Contour Similarity Algorithms [pdf]Paper   bibtex   abstract
Simulating melodic and harmonic expectations for tonal cadences using probabilistic models. Sears, D. R.; Pearce, M. T.; Caplin, W. E.; and McAdams, S. Journal of New Music Research, 47(1): 29–52. 2018.
Simulating melodic and harmonic expectations for tonal cadences using probabilistic models [link]Paper   doi   bibtex   abstract
Substantial musical similarity in sound and notation: Perspectives from digital musicology. Selfridge-Field, E. Colorado Technology Law Journal, 16(2): 249–284. 2018.
Substantial musical similarity in sound and notation: Perspectives from digital musicology [link]Paper   bibtex
Differentiae in the cantus manuscript database. Shaw, R. In Proceedings of the 5th International Conference on Digital Libraries for Musicology - DLfM '18, pages 38–46, New York, New York, USA, 2018. ACM Press
Differentiae in the cantus manuscript database [link]Paper   doi   bibtex   abstract
Computational Corpus Analysis: a Case Study on Jazz Solos. Weiß, C.; Balke, S.; Abeßer, J.; and Müller, M. In Proceedings of the 19th International Society for Music Information Retrieval, Paris, 2018.
Computational Corpus Analysis: a Case Study on Jazz Solos [pdf]Paper   bibtex   abstract
Investigating style evolution of Western classical music: A computational approach. Weiß, C.; Mauch, M.; Dixon, S.; and Müller, M. Musicae Scientiae, (March). 2018.
Investigating style evolution of Western classical music: A computational approach [link]Paper   doi   bibtex   abstract
  2017 (13)
The digital music lab: A big data infrastructure for digital musicology. Abdallah, S.; Benetos, E.; Gold, N.; Hargreaves, S.; Weyde, T.; and Wolff, D. Journal on Computing and Cultural Heritage, 10(1): 1–21. 2017.
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Sketching Sonata Form Structure in Selected Classical String Quartets. Bigo, L.; Giraud, M.; Groult, R.; Guiomard-Kagan, N.; and Levé, F. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), pages 752–759, Suzhou, China, 2017.
Sketching Sonata Form Structure in Selected Classical String Quartets [pdf]Paper   bibtex   abstract
Practical Statistics for Data Scientists. Bruce, P.; and Bruce, A. O'Reilly Media, Sebastopol, CA, 2017.
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PatternFinder: Content-Based Music Retrieval with music21. Garfinkle, D.; Arthur, C.; Schubert, P.; Cumming, J.; and Fujinaga, I. ACM International Conference Proceeding Series, (October): 5–8. 2017.
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Western classical music development: a statistical analysis of composers similarity, differentiation and evolution. Georges, P. Scientometrics, 112(1): 21–53. jul 2017.
Western classical music development: a statistical analysis of composers similarity, differentiation and evolution [link]Paper   doi   bibtex   abstract
Of beginnings and ends a corpus-based inquiry into the rise of the recapitulation. Greenberg, Y. Journal of Music Theory, 61(2): 171–200. 2017.
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Trimodular Block Strategies in Haydn's Sonata Movements by Samantha M. Inman. Inman, S. M. Haydn: Online Journal of the Haydn Society of North America, 7(Spring): 1–27. 2017.
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Finding Occurrences of Melodic Segments in Folk Songs Employing Symbolic Similarity Measures. Janssen, B.; van Kranenburg, P.; and Volk, A. Journal of New Music Research, 46(2): 118–134. apr 2017.
Finding Occurrences of Melodic Segments in Folk Songs Employing Symbolic Similarity Measures [link]Paper   doi   bibtex   abstract
Tonal Harmony: with an Introduction to Post-Tonal Music. Kostka, S. M.; Payne, D.; and Almén, B. McGraw-Hill Education, New York, 8 edition, 2017.
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A Case for Declassifying the IAC as a Cadence Type: Cadence and Thematic Design in Selected Early- to Middle-Period Haydn Sonatas. MacKay, J. S. Ad Parnassum, 15(30): 1–27. 2017.
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A computational study on outliers in world music. Panteli, M.; Benetos, E.; and Dixon, S. PLoS ONE, 12(12): 1–29. 2017.
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