Music Reading And The Complexity In The Score

An Ecological Validation Model For Complexity Measurements In Written Music

Keywords: music notation, complexity, perceptual span, working memory

Abstract

We present a model that allows the content of the score to be divided automatically, based on the latest insights in music reading, cognition, and complexity. By relating “perceptual breadth” and “working memory”, we are able to find precise thresholds, which can be used to assess the complexity of music reading. The model conforms to the ecological validity requirements for the calculations that evaluate the complexity in written music and the validation shows that it is applicable to easy and difficult fragments written with musical notation.

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Author Biographies

Pablo Padilla Longoria, Universidad Nacional Autónoma de México

Pablo Padilla studied Mathematics (Gabino Barreda medal) and Physics at the National Autonomous University of Mexico and piano at the National Conservatory of Music. He earned his M.Sc. and Ph.D. degrees from New York University's Courant Institute of Mathematical Sciences, as well as a Diploma in Piano from Mannes College of Music, where he also studied harpsichord. , composition and improvisation. He held a postdoctoral position at the Swiss Federal Polytechnic Institute in Zürich (ETH) and continued his harpsichord studies at the Higher School of Music in the same city with Mtra. Carmen Schibli. He has been a visiting professor at different universities, including the University of Oxford, the School of Higher Studies in Social Sciences (EHESS, Paris) and the University of Cambridge. He has given lectures at various institutions, including the U. Complutense of Madrid, the University of Granada, the Korean Institute for Advanced Studies (KIAS), the Normal School of Engineers of Tunisia (ENIT), the Universities of Rome, Chile, Keio (Japan ), the Instituto Superior Técnico de Lisboa, Harvard University, the Isaac Newton Institute of Cambridge, etc. He is currently tenured professor C in the Department of Mathematics and Mechanics of the Institute for Research in Applied Mathematics and Systems (IIMAS) of the UNA He teaches classes in the Faculty of Sciences and in the Faculty of Music, also at UNAM. His research interests include nonlinear differential equations, dynamical systems, the calculus of variations, and mathematics applied to biology and finance. Additionally, he has done research on mathematical models in archaeological acoustics (archaeoacoustics) and mathematical aspects of music (algorithmic composition and artificial intelligence methods applied to music). He is a member of the Mexican Academy of Sciences and the National System of Researchers.

María del Mar Galera-Núñez, Sevilla University

Mar Galera-Núñez is a doctor in Educational Sciences, with a higher degree in the specialties of: Piano, Chamber Music and Solfeggio and Music Theory; degree in Art History from the University of Seville; Expert in Methods and Resources of Musical Education from the University of La Laguna. He is a member of the Didactic Research Group (GID). His main lines of research are related to artistic languages ​​in education, music technology and music training for early childhood teachers. She has participated as a director and researcher in different research projects related to music teaching and has been a member of the scientific committee of different educational conferences, as well as a reviewer in magazines related to music and artistic education: LEEME, Educación XX1, Pedagogía Social, Electronic Journal of Research in Educational Psychology, among others. He has almost a hundred published works between communications, magazine articles, chapters and books related to music education.

References

Angeler, D. G. (2020). Biodiversity in Music Scores. Challenges, 11(1), 7. https://doi.org/10.3390/challe11010007

Ávila Cascajares, F. S. (2021). Capacidad de memoria y estrategias de instrumentistas y no músicos en pruebas auditivas de Sternberg modificadas con unidades significativas musicales y verbales. Tesis de maestría en cognición musical: UNAM.

Bennett, D., Gobet, F., & Lane, P. C. R. (2020). Forming Concepts of Mozart and Homer Using Short-Term and Long-Term Memory: A Computational Model Based on Chunking. In S. Denison, M. Mack, Y. Xu, & B. C. Armstrong (Eds.), Proceedings of the 42th Annual Meeting of the Cognitive Science Society—Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29—August 1, 2020.

Burman, D. D., & Booth, J. R. (2009). Music Rehearsal Increases the Perceptual Span for Notation. Music Perception, 26(4), 303–320. https://doi.org/10.1525/mp.2009.26.4.303

Chang, T.-Y., & Gauthier, I. (2021). Domain-specific and domain-general contributions to reading musical notation. Attention, Perception, & Psychophysics. https://doi.org/10.3758/s13414-021-02349-3

Chase, I. D. (2006). Music notation: A new method for visualizing social interaction in animals and humans. Frontiers in Zoology, 3(1), 18. https://doi.org/10.1186/1742-9994-3-18

Chitalkina, N., Puurtinen, M., Gruber, H., & Bednarik, R. (2020). Handling of incongruences in music notation during singing or playing. International Journal of Music Education, 39(1), 18–38. https://doi.org/10.1177/0255761420944036

Dover publications. (1983). Ludwig van Beethovens Werke, Serie 9, Nr.67. Leipzig: Breitkopf und Härtel, n.d.[1862]. Plate B.67. dominio público, acceso desde https://imslp.org/wiki/Piano_Concerto_No.3%2C_Op.37_(Beethoven%2C_Ludwig_van), acceso 2022-01-01.

Eden Ünlü, S., & Ece, A. S. (2019). Reading notation with Gestalt perception principles: Gestalt algı ilkeleri ile notasyon okuma. Journal of Human Sciences, 16(4), 1104–1120. https://doi.org/10.14687/jhs.v16i4.5822

Gunter, T. C., Schmidt, B.-H., & Besson, M. (2003). Let’s face the music: A behavioral and electrophysiological exploration of score reading. Psychophysiology, 40(5), Article 5. https://doi.org/10.1111/1469-8986.00074

Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

Holder, E., Tilevich, E., & Gillick, A. (2015). Musiplectics: Computational assessment of the complexity of music scores. 2015 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!) - Onward! 2015, 107–120. https://doi.org/10.1145/2814228.2814243

Ishiguro, Y., & Rekimoto, J. (2011). Peripheral vision annotation: Noninterference information presentation method for mobile augmented reality. Proceedings of the 2nd Augmented Human International Conference on - AH ’11, 1–5. https://doi.org/10.1145/1959826.1959834

Janurik, M., Surján, N., & Józsa, K. (2022). The Relationship between Early Word Reading, Phonological Awareness, Early Music Reading and Musical Aptitude. Journal of Intelligence, 10(3), 50. https://doi.org/10.3390/jintelligence10030050

Laske, O. E. (1988). Introduction to Cognitive Musicology. Computer Music Journal, 12(1), 43. https://doi.org/10.2307/3679836

Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. MIT Press.

Lopes, A. M., & Tenreiro Machado, J. A. (2019). On the Complexity Analysis and Visualization of Musical Information. Entropy, 21(7), 669. https://doi.org/10.3390/e21070669

Malbrán, S. (2007). El oído de la mente. Akal.

McConkie, G. W., & Rayner, K. (1975). The span of the effective stimulus during a fixation in reading. Perception & Psychophysics, 17(6), 578–586. https://doi.org/10.3758/BF03203972

Mills, J., & McPherson, G. E. (2015). Musical literacy: Reading traditional clef notation. En McPherson, G. E. (Ed.), The Child as Musician (pp. 177–191). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198744443.003.0009

Morgan, E., Fogel, A., Nair, A., & Patel, A. D. (2019). Statistical learning and Gestalt-like principles predict melodic expectations. Cognition, 189, 23–34. https://doi.org/10.1016/j.cognition.2018.12.015

Pease, A., Mahmoodi, K., & West, B. J. (2018). Complexity measures of music. Chaos, Solitons & Fractals, 108, 82–86. https://doi.org/10.1016/j.chaos.2018.01.021

Puurtinen, M. (2018). Eye on Music Reading: A Methodological Review of Studies from 1994 to 2017. Journal of Eye Movement Research, 11(2). https://doi.org/10.16910/jemr.11.2.2

Schultz, M. (1998). La notación musical desde la perspectiva semiótica. Arte e Investigación, 2(2), 44–47.

Sheridan, H., & Kleinsmith, A. L. (2021). Music reading expertise affects visual change detection: Evidence from a music-related flicker paradigm. Quarterly Journal of Experimental Psychology, 174702182110569. https://doi.org/10.1177/17470218211056924

Silva, S., & Castro, S. L. (2019). The time will come: Evidence for an eye-audiation span in silent music reading. Psychology of Music, 47(4), 504–520. https://doi.org/10.1177/0305735618765302

Sloboda, J. A. (1976). The effect of item position on the likelihood of identification by inference in prose reading and music reading. Canadian Journal of Psychology/Revue Canadienne de Psychologie, 30(4), Article 4. https://doi.org/10.1037/h0082064

Stenberg, A., & Cross, I. (2019). White spaces, music notation and the facilitation of sight-reading. Scientific Reports, 9(1), Article 1. https://doi.org/10.1038/s41598-019-41445-1

Stewart, L. (2005). A Neurocognitive Approach to Music Reading. Annals of the New York Academy of Sciences, 1060(1), Article 1. https://doi.org/10.1196/annals.1360.032

Valle, F. (1985). El problema de la validez ecológica. Estudios de Psicología, 6(23–24), 135–151. https://doi.org/10.1080/02109395.1985.10821439

Viljoen, J. F., & Foxcroft, C. (2020). Gaze Patterns of Skilled and Unskilled Sight Readers Focusing on the Cognitive Processes Involved in Reading Key and Time Signatures. International Journal of Humanities and Social Sciences, 14(9), 764–767. https://publications.waset.org/vol/165

Weiss, C., Balke, S., Abeßer, J., & Müller, M. (2018). Computational Corpus Analysis: A Case Study on Jazz Solos. https://doi.org/10.5281/ZENODO.1492439

Published
2023-07-05
How to Cite
Calatayud, P., Padilla Longoria, P., Galera-Núñez, M. del M., & Pérez-Acosta, G. (2023). Music Reading And The Complexity In The Score. MAGOTZI Boletín Científico De Artes Del IA, 11(22), 26-33. https://doi.org/10.29057/ia.v11i22.10638