Musiplectics: computational assessment of the complexity of music scores

Abstract

In the Western classical tradition, musicians play music from notated sheet music, called a score. When playing music from a score, a musician translates its visual symbols into sequences of instrument-specific physical motions. Hence, a music score's overall complexity represents a sum of the cognitive and mechanical acuity required for its performance. For a given instrument, different notes, intervals, articulations, dynamics, key signatures, and tempo represent dissimilar levels of difficulty, which vary depending on the performer's proficiency. Individual musicians embrace this tenet, but may disagree about the degrees of difficulty. This thesis introduces musiplectics1 , a systematic and objective approach to computational assessment of the complexity of a music score for any instrument. Musiplectics defines computing paradigms for automatically and accurately calculating the complexity of playing a music score on a given instrument. The core concept codifies a two-phase process. First, music experts rank the relative difficulty of individual musical components (e.g., notes, intervals, dynamics, etc.) for different playing proficiencies and instruments. Second, a computing engine automatically applies this ranking to music scores and calculates their respective complexity. As a proof of concept of musiplectics, we present an automated, Web-based application called Musical Complexity Scoring (MCS) for music educators and performers. Musiplectics can engender the creation of practical computing tools for objective and expeditious assessment of a music score's suitability for the abilities of intended performers.This thesis is based on research submitted for publication at ONWARD'15.That this work received support from a Virginia Tech ICAT SEAD grant.1 musiplectics = music + plectics, Greek for the study of complexity DedicationTo the memory of Coppertone Holder, the standard by which all other pets shall be measured.iii Acknowledgments I have not experienced anything in this world quite like the publication of my master's thesis. It has taken only my last two years of graduate school to accomplish, but it has brought me (kicking and screaming at times) further along in those two years than I could have possibly imagined. This process took its toll many times not only on myself, but on all of those close to me. Thus, I'd like to take a moment to graciously acknowledge and thank all of those that had a hand in both shaping my research and keeping my sanity.

Evolving Musical Sight Reading Exercises Using Expert Models

“…These models would be relative to a specific instrument. One possible model is the musiplectics system ( Holder et al, 2015 ). In this work, Holder et al (2015) defines a method for computationally measuring the complexity of a musical score for any instrument.…”

“…One possible model is the musiplectics system ( Holder et al, 2015 ). In this work, Holder et al (2015) defines a method for computationally measuring the complexity of a musical score for any instrument. Measuring the complexity of a score first requires the definition of several parameters for the chosen instrument.…”

Designing Computationally Creative Musical Performance Systems

“…Using a a measure for how complex, or challenging a musical piece is, such as that of musicplectics by Holder et al [14] may seem redundant when also utilizing a perceptual model of performance. However, as a virtuosic performance is more likely to be produced by a virtuoso, and these will be technically capable musicians, having a measure of performance complexity let us acknoweledge and account for this within E. By having this complexity value as a weighting applied to our perceptual model, simple musical pieces can have the likelihood for virtuosity increase by performing the piece with more complex techniques, and vice versa.…”

La lectura musical y la complejidad en la partitura

“…Aquellos modelos terminan arrojando números que tienen poca relación significativa con lo que están midiendo. Por ejemplo, la implementación de varias estrategias de complejidad para encontrar patrones en música (Lopes & Tenreiro, 2019), la detección de patrones de complejidad el estudio de la evolución tonal en grandes corpus de jazz (Weiss et al, 2018), el análisis de biodiversidad en partituras musicales (Angeler, 2020), representaciones cuantitativas visuales con notación musical (Chase, 2006) o algoritmos para medir complejidad sintáctica en música (Holder, 2015) utilizan la capacidad computacional para calcular datos, pero no toman en cuenta la capacidad humana para hacerlo. Probablemente admiten que el modelo que utiliza memoria computacional es compatible con la forma en que las neuronas producen actividad cerebral.…”