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Algorithmically Assisted Improvised Music

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Algorithmically Assisted Improvised Music University of Calgary PRISM: University of Calgary's Digital Repository Graduate Studies The Vault: Electronic Theses and Dissertations 2016 AAIM: Algorithmically Assisted Improvised Music Fay, Simon Fay, S. (2016). AAIM: Algorithmically Assisted Improvised Music (Unpublished doctoral thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/24629 http://hdl.handle.net/11023/3073 doctoral thesis University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. Downloaded from PRISM: https://prism.ucalgary.ca UNIVERSITY OF CALGARY AAIM: Algorithmically Assisted Improvised Music by Simon Fay A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN COMPUTATIONAL MEDIA DESIGN CALGARY, ALBERTA June, 2016 c Simon Fay 2016 Abstract The AAIM (Algorithmically Assisted Improvised Music) performance system1 is a portfolio of interconnectable algorithmic software modules, designed to facilitate improvisation and live per- formance of electronic music. The AAIM system makes no attempt to generate new materials in one particular style, nor to act as an autonomous improviser. Instead, the goal of the AAIM sys- tem is to facilitate improvisation through the variation and manipulation of composed materials entered by the user. By generating these variations algorithmically, the system gives improvisers of computer music the ability to focus on macro elements of their performances, such as form, phrasing, texture, spatialisation, and timbre, while still enabling them to incorporate the rhythmic and melodic variations of a virtuosic instrumental improviser. 1https://simonjohnfay.com/aaim/ ii Acknowledgements First I would like to thank my supervisors David Eagle and Jeff Boyd. Their assistance, sugges- tions, and support throughout my research was invaluable. I also want to give a special thanks my friends and collaborators Lawrence Fyfe, Aura Pon, and Ethan Cayko who all worked with me on a number of pieces using AAIM. Finally, I want to thank all my friends and family who have always supported and encouraged me. iii Table of Contents Abstract ........................................... i Acknowledgements .................................... ii Table of Contents . iii List of Tables . viii List of Figures . ix List of Symbols . xii 1 Introduction . 1 1.1 Advancing Technologies and Musical Developments . 2 1.2 Motivation . 5 1.3 Scope . 6 1.4 Goal . 7 1.5 Challenges . 7 1.6 Methodology . 9 1.7 Contributions . 11 1.8 Thesis Structure . 14 2 Musical Context . 15 2.1 Electronic Music . 15 2.1.1 Musique Concrete` ........................................ 19 2.1.2 Elektronische Musik ........................................ 20 2.1.3 BBC Radiophonic Workshop, and Delia Derbyshire ........................................ 20 2.1.4 Modular Synthesisers, and Morton Subotnick ........................................ 21 2.1.5 Feedback Delay, Live Sampling, Terry Riley, Kaffe Mathews ........................................ 23 2.1.6 FM Synthesis, and Paul Lansky ........................................ 25 2.1.7 Sampling, Breakbeats, John Oswald, Plunderphonics ........................................ 26 2.1.8 Techo and The “New Generation” ........................................ 27 2.1.9 Aphex Twin ........................................ 29 2.1.10 Autechre ........................................ 31 2.1.11 Squarepusher ........................................ 33 2.1.12 Matmos and Bjork¨ ........................................ 35 iv 2.1.13 Richard Devine ........................................ 37 2.1.14 Summary ........................................ 38 2.2 Improvisation . 39 2.2.1 Improvisation and composition as two points on a continuum . 40 2.2.2 The use of ‘referents’ or ‘models’ in improvisation . 41 2.2.3 Improvisation as the re-use, variation, and manipulation of previously learnt ‘building blocks’ . 43 2.3 Algorithms and Computational Thinking in Music . 45 2.3.1 Algorithms in improvised music ........................................ 51 3 Related Work - Algorithmic Music Performance Systems . 55 3.1 Algorithmic Methods . 55 3.1.1 Mathematical Models . 56 3.1.2 Knowledge-based methods . 57 3.1.3 Grammars . 57 3.1.4 Evolutionary Methods . 58 3.1.5 Systems That Learn . 58 3.1.6 Hybrid Systems . 59 3.2 Other Means of Categorisation . 59 3.2.1 Algorithmic Function . 59 3.2.2 Instrument v Player Paradigms . 60 3.2.3 Musical ‘problem space’ . 60 3.3 Overview . 61 3.3.1 Player Paradigm Systems . 61 3.3.1.1 Voyager . 61 3.3.1.2 prosthesis . 62 3.3.1.3 Continuator . 62 3.3.1.4 OMAX-OFON . 63 3.3.1.5 GenJam . 64 3.3.1.6 Kinetic Engine . 64 3.3.1.7 GEDMAS . 65 3.3.2 Instrument Paradigm Systems . 66 3.3.2.1 Lexikon-Sonate . 66 3.3.2.2 Virtuoso . 66 3.3.2.3 Syncopalooza . 67 3.3.2.4 Liquid Music . 67 3.3.2.5 2020 Beat-Machine . 68 3.3.2.6 ITVL . 69 3.3.2.7 Fugue Machine . 69 3.4 Discussion . 70 4 AAIM . 72 4.1 Concept . 73 4.2 Artistic Goals . 76 v 4.3 Design Philosophy . 78 4.3.1 Algorithmic Approach . 81 4.4 Improvisation with AAIM ............................... 85 4.4.1 Composition - Improvisation Continuum . 85 4.4.2 Referent-based Improvisation . 86 4.4.3 Improvisation through the re-use, variation, and manipulation of ‘building blocks’ . 86 5 Software Portfolio . 88 5.1 AAIM.rhythmGen ................................... 89 5.1.1 Input and Interaction . 89 5.1.2 Trigger Output . 91 5.1.3 ioiChooser . 92 5.1.4 Indispensability . 94 5.1.5 Additional Features . 98 5.1.5.1 Tempo Changes . 98 5.1.5.2 Timing Variations . 98 5.2 AAIM.patternVary ................................... 99 5.2.1 Input and Interaction . 99 5.2.2 Trigger Output and Basic Variations . 100 5.2.3 Inserting ‘Extra’ Notes . 100 5.2.4 Additional Features . 101 5.2.4.1 Maximum Notes . 101 5.2.4.2 Grouping Determination . 101 5.3 AAIM.loopVary ....................................102 5.3.1 Input and Interaction . 102 5.3.2 Grain Size . 105 5.3.3 Reverse . 105 5.3.4 Retrigger . 106 5.3.5 Jump . 107 5.4 AAIM.melodyVary ...................................107 5.4.1 Input and Interaction . 108 5.4.1.1 Available Pitches . 109 5.4.1.2 Actual Pitches . 109 5.4.1.3 Modal Mapping . 110 5.4.2 Melodic Analysis . 110 5.4.2.1 Interval Content . 112 5.4.2.2 Trajectory . 112 5.4.2.3 Possible Notes . 113 5.4.3 Inverse . 114 5.4.4 Retrograde . 114 5.4.5 Retrograde-Inversion . 114 5.4.6 Expansion . 114 5.4.7 Sequence . 116 5.4.8 Scalar Notes ..
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