UC San Diego UC San Diego Electronic Theses and Dissertations Title Compositional and analytic applications of automated music notation via object-oriented programming Permalink https://escholarship.org/uc/item/3kk9b4rv Author Trevino, Jeffrey Robert Publication Date 2013 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, SAN DIEGO Compositional and Analytic Applications of Automated Music Notation via Object-oriented Programming A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Music by Jeffrey Robert Trevino Committee in charge: Professor Rand Steiger, Chair Professor Amy Alexander Professor Charles Curtis Professor Sheldon Nodelman Professor Miller Puckette 2013 Copyright Jeffrey Robert Trevino, 2013 All rights reserved. The dissertation of Jeffrey Robert Trevino is approved, and it is acceptable in quality and form for publication on mi- crofilm and electronically: Chair University of California, San Diego 2013 iii DEDICATION To Mom and Dad. iv EPIGRAPH Extraordinary aesthetic success based on extraordinary technology is a cruel deceit. —-Iannis Xenakis v TABLE OF CONTENTS Signature Page .................................... iii Dedication ...................................... iv Epigraph ....................................... v Table of Contents................................... vi List of Figures .................................... ix Acknowledgements ................................. xii Vita .......................................... xiii Abstract of the Dissertation . xiv Chapter 1 A Contextualized History of Object-oriented Musical Notation . 1 1.1 What is Object-oriented Programming (OOP)? . 1 1.1.1 Elements of OOP . 1 1.1.2 ANosebleed History of OOP. 6 1.2 Object-oriented Notation for Composers . 12 1.2.1 Composition as Notation . 12 1.2.2 Generative Task as an Analytic Framework . 13 1.2.3 Computational Models of Music/Composition . 14 1.2.4 Computational Models of Notation . 16 1.2.5 Object-oriented Systems . 17 1.2.6 Graphical Object-oriented Programming Systems . 19 1.3 Design Values for Automated Notation Systems, Illus- trated with the Abjad API for Formalized Score Control . 23 1.3.1 The Abjad API for Formalized Score Control . 23 1.3.2 Design Recommendations . 27 Chapter2 Computational Modeling as Analysis . 38 2.1 The Conflation of Analysis and Composition Reveals and Posits Construction . 38 2.1.1 Formalization Reveals Metaphor . 38 2.2 Reverse Engineering as Analysis: Two Case Studies in For- malized Score Control as Analysis . 40 2.2.1 Cantus in Memory of Benjamin Britten (1977-1980) by Arvo Pärt........................ 40 2.2.2 Windungen (1976) by Iannis Xenakis . 57 vi 2.3 Revealed Strengths and Weaknesses of Formalized Score Control ............................. 81 Chapter 3 Automated Notation for the Analysis of Recorded Music . 84 3.1 Background .......................... 84 3.2 Methodology for Representing Amplitude and Onset Time as Notation........................... 85 3.3 Conclusion and Future Directions for Research . 98 Chapter4 Compositional Applications . 100 4.1 Algorithmic Tendencies,2004—2008 . 101 4.1.1 Substitute Judgment (2004) for Solo Percussionist . 102 4.1.2 Binary Experiment for James Tenney (2005) for Four Contrabasses..... .... ..... .... .... 104 4.1.3 Mobile (2005) for Tenor Saxophone . 106 4.1.4 Zoetropes (2005—6) for Bass Clarinet, Cello, and Percussion ....................... 108 4.1.5 Unit for Convenience and Better Living 003 (2006) for Solo Bass Clarinet . 110 4.1.6 Mexican Apple Soda (Consumer Affect Simulation I.1) (2006) for Contrabass and Chamber Ensemble . 111 4.1.7 Mexican Apple Soda Paraphrase (2007) for Contrabass and Live Electronics. 112 4.1.8 Perfection Factory (2008) for Two Percussonists . 112 4.2 Installation and Visual Music,2009—2010 . 114 4.2.1 Algorithmically Generated Trees (2009). 114 4.2.2 Blooms (2010)...................... 116 4.3 Computer-assisted Works,2010—2013 . 119 4.3.1 Being Pollen (2010—2011) for Solo Percussion . 119 4.3.2 +/- (2011—2012) for Twenty French Horns . 121 4.3.3 The World All Around (2013) for Harp, Clarinet, and Piano.......................... 124 4.4 Conclusion........................... 128 AppendixA Code Examples . 131 A.1 Abjad Interface to Mike Solomon’s LilyPond Woodwind Diagrams As a Function, Implemented with Basic String Functions............................ 132 A.2 Abjad Interface to Mike Solomon’s LilyPond Woodwind Diagrams As a Function, Implemented with Abjad Scheme Functions............................ 133 vii A.3 Abjad Interface to Mike Solomon’s LilyPond Woodwind Diagrams As the WoodwindDiagram Class, Inheriting from Abjad’s AbjadObject Abstract Class . 134 A.4 Processing Code for Algorithmically Generated Trees ..... 143 A.5 Catalogue of Possible Entrances Into and Exits from Clar- inet Multiphonics . 155 A.6 Clarinet Solo Material Based on Multiphonic Catalogue . 160 A.7 Prepared Piano Part for The World All Around ........ 165 A.8 Harp Part for The World All Around .............. 171 A.9 Formatted Score for The World All Around .......... 174 AppendixB Score Examples . 176 B.1 Arvo Pärt’s Cantus in Memory of Benjamin Britten (1977—80) for Bell and String Orchestra, as Rendered with the Abjad APIfor Formalized Score Control . 177 B.2 Iannis Xenakis’s Windugen (1976) for Twelve Cellos, as Rendered with the Abjad API for Formalized Score Con- trol ............................... 183 B.3 Glenn Gould’s Performances of the First Movement of We- bern’s op.27 Piano Variations . 191 B.4 The World All Around (2013) for Prepared Piano, Eb Clar- inet,and Harp ......................... 196 viii LIST OF FIGURES Figure 1.1: Abjad creates notation by scripting the LilyPond typesetting pro- gram. ................................. 24 Figure1.2: Rest-delimited notes and chords. 28 Figure 1.3: Code to slur groups of rest-delimited notes and chords in PWGL/ENP............................... 28 Figure 1.4: Slurred groups of rest-delimited notes and chords. 29 Figure 1.5: A multiphonic notation, including a woodwind diagram. 33 Figure 1.6: Graphic overrides change the appearance of a woodwind diagram. 35 Figure2.1: Afont function enables custom typefaces. 41 Figure 2.2: Modeling the bell and string staffs and their names. 42 Figure2.3: Adding string staffs to a score. 43 Figure2.4: Modeling the bell part. 44 Figure2.5: Modeling pitch as a series of scalar descents. 45 Figure 2.6: Modeling the pitches: a switch system for choosing arpeggio notes. ................................. 46 Figure 2.7: Applying the arpeggio notes to the scalar descents. 47 Figure 2.8: Recursively generating most of the string parts’ rhythms. 48 Figure 2.9: Splitting durations cyclically by the duration of one bar. 48 Figure 2.10: Manual composition of pitches and rhythms after generation. 50 Figure2.11: Splitting and finishing the viola part. 51 Figure 2.12: The cello and contrabass pitches and rhythms composed to com- pletion. ................................ 52 Figure 2.13: Placing previously generated pitches and rhythms into measures. 53 Figure 2.14: Adding dynamic markings to parts via measure indexes. 54 Figure 2.15: Adding technical and expressive markings to parts via measure indexes. ................................ 55 Figure 2.16: Defining and using a custom technical marking. 56 Figure2.17: Adding rehearsal marks. 56 Figure2.18: Document layout and formatting. 57 Figure 2.19: Modeling the rotation of material through the score. 59 Figure 2.20: A function that returns a matrix of cyclic tuples to specify which staffs the rotating music should be written on. 60 Figure2.21: Modeling Xenakis’s bookended rotations. 60 Figure2.22: From single staff to rotating staffs. 61 Figure2.23: Repitching the staff as it rotates. 62 Figure 2.24: Modeling the low-level typographical habits in the rotation sec- tion. .................................. 63 Figure 2.25: Function for creating a staff of random pitches. 64 Figure 2.26: The final stage of typographical adjustment for the rotation sec- tion. .................................. 65 ix Figure2.27: The final rotation function. 66 Figure2.28: Utility functions enable rotation. 66 Figure 2.29: Modeling the score’s first rotation with the rotation function. 67 Figure 2.30: Modeling a rotation with multiple simultaneous pitches. 68 Figure 2.31: Modeling the tutti section with randomly selected sixteenth notes. ................................. 69 Figure2.32: The tutti section as single function. 70 Figure 2.33: The first section of the score as a single encapsulation. 70 Figure 2.34: Weighted probability choice functions for the first random walk section. ................................ 71 Figure 2.35: Conditional checks to determine a clef change. 72 Figure 2.36: Applying automatic clef changes to an expression. 73 Figure2.37: Adding random walk notes to the score. 74 Figure2.38: Querying and fusing trill spanners. 75 Figure 2.39: Functions for adding drones and random walks. 76 Figure 2.40: Functions for adding the random walk gesture to score, framed by drones as specified. 77 Figure2.41: The random walk section as a single function. 78 Figure 2.42: Imposing metric hierarchy by fusing chains of small durations. 79 Figure2.43: Imposing metric hierarchy on the entire score. 80 Figure2.44: Creating the score object. 81 Figure3.1: Reading recording data from file in Python. 87 Figure3.2: Processing recording data in Python. 88 Figure3.3: Reading pitch data from the