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1

Yust, Jason, Jonathan Wild, and John Ashley Burgoyne, eds. Mathematics and Computation in Music. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39357-0.

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2

Klouche, Timour, and Thomas Noll, eds. Mathematics and Computation in Music. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04579-0.

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3

Collins, Tom, David Meredith, and Anja Volk, eds. Mathematics and Computation in Music. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20603-5.

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4

Agon, Carlos, Moreno Andreatta, Gérard Assayag, Emmanuel Amiot, Jean Bresson, and John Mandereau, eds. Mathematics and Computation in Music. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21590-2.

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5

Chew, Elaine, Adrian Childs, and Ching-Hua Chuan, eds. Mathematics and Computation in Music. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02394-1.

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6

Agustín-Aquino, Octavio A., Emilio Lluis-Puebla, and Mariana Montiel, eds. Mathematics and Computation in Music. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71827-9.

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7

Montiel, Mariana, Francisco Gomez-Martin, and Octavio A. Agustín-Aquino, eds. Mathematics and Computation in Music. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21392-3.

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8

A-life for music: Music and computer models of living systems. A-R Editions, 2011.

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9

Elaine, Chew, Childs, Adrian P. (Adrian Phillip), 1971-, and Chuan Ching-Hua, eds. Mathematics and computation in music: Second international conference, MCM 2009, John Clough Memorial Conference, New Haven, CT, USA, June 19 - 22, 2009, proceedings. Springer, 2009.

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10

Agon, Carlos. Mathematics and Computation in Music: Third International Conference, MCM 2011, Paris, France, June 15-17, 2011. Proceedings. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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11

Yust, Jason. Mathematics and Computation in Music: 4th International Conference, MCM 2013, Montreal, QC, Canada, June 12-14, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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12

Timour, Klouche, and Noll, Thomas, 1962 Dec. 25-, eds. Mathematics and computation in music: First international conference, MCM 2007, Berlin, Germany, May 18-20, 2007 : revised selected papers. Springer, 2009.

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13

Meredith, David, ed. Computational Music Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25931-4.

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14

Chakraborty, Soubhik, Guerino Mazzola, Swarima Tewari, and Moujhuri Patra. Computational Musicology in Hindustani Music. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11472-9.

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15

G, Mazzola, Noll Thomas 1962-, Lluis-Puebla Emilio 1952-, International Seminar on Mathematical Music Theory (2nd : 2001 : Sauen, Germany), and International Seminar on Mathematical Music Theory and Music Informatics (3rd : 2002 : Zurich, Switzerland), eds. Perspectives in mathematical and computational music theory. Electronic Publishing Osnabrück, 2004.

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16

Ekárt, Anikó, Antonios Liapis, and María Luz Castro Pena, eds. Computational Intelligence in Music, Sound, Art and Design. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16667-0.

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17

Liapis, Antonios, Juan Jesús Romero Cardalda, and Anikó Ekárt, eds. Computational Intelligence in Music, Sound, Art and Design. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77583-8.

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18

Correia, João, Vic Ciesielski, and Antonios Liapis, eds. Computational Intelligence in Music, Sound, Art and Design. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55750-2.

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19

Rowe, Robert. Interactive music systems: Machine listening and composing. MIT Press, 1993.

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20

Kausel, Wilfried. A musical acoustician's guide to computational physics: Concepts, algorithms & applications. Institute of Musical Acoustics, University of Music, 2003.

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21

IEEE Symposium on Computational Intelligence in Scheduling (2009 Nashville, Tenn.). 2009 IEEE Symposium on Computational Intelligence in Scheduling (CI-Sched 2009) proceedings: March 30-April 2, 2009, Sheraton Music City Hotel, Nashville, TN, USA. IEEE, 2009.

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22

1951-, Feichtinger Hans G., Dörfler M, European Mathematical Society, Österreichische Mathematische Gesellschaft, and Universität Wien Mathematischer Zirkel, eds. Diderot Forum on Mathematics and Music: Computational and mathematical methods in music : Vienna, Austria, December 2-4, 1999 (EMS, ÖMG, Mathematischer Zirkel der Univ. Wien). Österreichische Computer Gesellschaft, 1999.

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23

International Workshop on the Cognitive Science of Natural Language Processing (8th 1999 Galway, Ireland). Language, vision, and music: Selected papers from the 8th International Workshop on the Cognitive Science of Natural Language Processing, Galway, Ireland, 1999. John Benjamins Pub., 2002.

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24

Bader, Rolf. Sound - Perception - Performance. Springer International Publishing, 2013.

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25

Mark, d’Inverno, and SpringerLink (Online service), eds. Computers and Creativity. Springer Berlin Heidelberg, 2012.

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26

Brown, Andrew R. Algorithms and Computation in Music Education. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.17.

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The chapter discusses how bringing music and computation together in the curriculum offers socially grounded contexts for the learning of digital expression and creativity. It explores how algorithms codify cultural knowledge, how programming can assist students in understanding and manipulating cultural norms, and how these can play a part in developing a student’s musicianship. In order to highlight how computational thinking extends music education and builds on interdisciplinary links, the chapter canvasses the challenges, and solutions, involved in learning through algorithmic music. Prac
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27

(Editor), Eduardo Reck Miranda, and John Al Biles (Editor), eds. Evolutionary Computer Music. Springer, 2007.

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28

Dean, Roger T., and Alex McLean, eds. The Oxford Handbook of Algorithmic Music. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.001.0001.

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Algorithmic music appears to be at a turning point in its history, with many new systems and communities of practice developing together, as vibrant musical culture. This handbook brings together dozens of leading researchers and practitioners in the field, blending technical, artistic, cultural and scientific viewpoints into a whole that considers the making of algorithmic music as a rich, and essentially human activity. The book is organised into four sections, the first grounding the topic in the history, philosophy and psychology of algorithmic music. The second section asks 'what can algo
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29

Mathematics and Computation in Music: 5th International Conference, MCM 2015, London, UK, June 22-25, 2015, Proceedings. Springer, 2015.

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30

Agon, Carlos, Emmanuel Amiot, and Moreno Andreatta. Mathematics and Computation in Music: Third International Conference, MCM 2011, Paris, France, June 15-17, 2011. Proceedings. Springer, 2011.

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31

Yust, Jason, Jonathan Wild, and John Ashley Burgoyne. Mathematics and Computation in Music: 4th International Conference, MCM 2013, Montreal, Canada, June 12-14, 2013, Proceedings. Springer, 2013.

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32

Lluis-Puebla, Emilio, Octavio A. Agustín-Aquino, and Mariana Montiel. Mathematics and Computation in Music: 6th International Conference, MCM 2017, Mexico City, Mexico, June 26-29, 2017, Proceedings. Springer, 2017.

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33

Meredith, David. Computational Music Analysis. Springer, 2015.

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34

Meredith, David. Computational Music Analysis. Springer, 2016.

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35

Collins, Nick. Origins of Algorithmic Thinking in Music. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.2.

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Musicians’ relationships with algorithms have deep precedents in the confluence of music and mathematics across millennia and across cultures. Technological and musico-mathematical precedents in the ancient world predate the Arabic etymology of the term ‘algorithm’. From Guido d’Arezzo’s hand to rule systems in music theory and eighteenth-century ars combinatoria, there is a rich background to twentieth-century rule-led music making. Robotic music, too, has precedents, and there is an interesting proto-computational thread linking the automata builder Vaucanson to early programmable weaving lo
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36

Mazzola, Guerino, Soubhik Chakraborty, Swarima Tewari, and Moujhuri Patra. Computational Musicology in Hindustani Music. Springer, 2016.

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37

Computational Musicology in Hindustani Music. Springer, 2014.

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38

Greher, Gena R., and Jesse M. Heines. Computational Thinking in Sound. Oxford University Press, 2014. http://dx.doi.org/10.1093/oso/9780199826179.001.0001.

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With Computational Thinking in Sound, veteran educators Gena R. Greher and Jesse M. Heines provide the first book ever written for music fundamentals educators that is devoted specifically to music, sound, and technology. Using a student-centered approach that emphasizes project-based experiences, the book provides music educators with multiple strategies to explore, create, and solve problems with music and technology in equal parts. It also provides examples of hands-on activities that encourage students, alone and in groups, to explore the basic principles that underlie today's music techno
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39

Gérard, Assayag, Gerzso Andrew, and IRCAM (Research institute : France), eds. New computational paradigms for computer music. Delatour, 2009.

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40

Gérard, Assayag, Gerzso Andrew, and IRCAM (Research institute : France), eds. New computational paradigms for computer music. Delatour, 2009.

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41

McLean, Alex, and Roger T. Dean. Algorithmic Trajectories. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.37.

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This chapter discusses the contrasting creative experiences of the two editors of this volume on algorithmic music, two complementary people from very different generations and musical backgrounds. One is an experienced instrumentalist with conventional musical training, who has run an international intermedia creative ensemble for several decades. He came to algorithms so as to extend his musical practice, in part by listening. The other is primarily a computer musician, with more training in computation than instrumental performance, and who conversely came to music to extend his algorithmic
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42

Young, Michael, and Tim Blackwell. Live Algorithms for Music. Edited by Benjamin Piekut and George E. Lewis. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199892921.013.002.

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Live algorithms are an ideal concept: computational systems able to collaborate proactively with humans in the creation of group-based improvised music. The challenge is to achieve equivalence between human and computer collaborators, both in formal terms and in practice (evident to both performers and audience alike). The fundamental question is the capacity for computational processes to exhibit “creativity.” The problems inherent in computer music performance are considered, in which computers are quasi-instruments or act in proxy for another musician. Theories from social psychology and pr
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43

Wiggins, Geraint A., Marcus T. Pearce, and Daniel Müllensiefen. Computational Modeling of Music Cognition and Musical Creativity. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199792030.013.0019.

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44

Miranda, Eduardo Reck. Guide to Unconventional Computing for Music. Springer, 2017.

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45

Miranda, Eduardo Reck. Guide to Unconventional Computing for Music. Springer, 2018.

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46

Eldridge, Alice, and Oliver Bown. Biologically Inspired and Agent-Based Algorithms for Music. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.18.

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This chapter examines a range of approaches to algorithmic music making inspired by biological systems, and considers topics at the intersection of contemporary music, computer science, and computational creativity. A summary of core precursor movements both within and beyond musical practice (A Life, cybernetics, systems art, etc.) sets the scene, before core models and algorithms are introduced and illustrated. These include evolutionary algorithms, agent-based modelling and self-organizing systems, adaptive behaviour and interactive performance systems, and ecosystemic approaches to composi
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47

Lazzarini, Victor. Spectral Music Design. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197524015.001.0001.

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This volume offers a complete guide to a computational approach to spectral music-making. It provides, in a stepwise manner, a [ ] to the signal processing techniques and their application to computer music. The book begins with a series of fundamental definitions, delineating the basic concepts of spectral audio. This includes both a technical and a historical appreciation of the ideas related to the spectrum. The core of the text is formed by six chapters on the techniques of spectral musical signal processing. These are thoroughly illustrated with examples and code excerpts using the Python
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48

Mazzola, Guerino, Octavio Alberto Agustín-Aquino, and Julien Junod. Computational Counterpoint Worlds: Mathematical Theory, Software, and Experiments. Springer, 2016.

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49

Mazzola, Guerino, Octavio Alberto Agustín-Aquino, and Julien Junod. Computational Counterpoint Worlds: Mathematical Theory, Software, and Experiments. Springer, 2015.

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50

Tenney, James. Excerpts from “An Experimental Investigation of Timbre—the Violin”. Edited by Larry Polansky, Lauren Pratt, Robert Wannamaker, and Michael Winter. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252038723.003.0005.

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James Tenney presents excerpts from his 1966 essay “An Experimental Investigation of Timbre—the Violin.” The research was carried out at the School of Music and the Computation Center at Yale University. Tenney first provides a description of the experiment as well as the equipment and computer programs he used in his investigation. In particular, he discusses the basic approach to sound analysis and synthesis that employs a digital computer with peripheral equipment for translating a signal from “analog” to digital form (for analysis) and from digital to analog form (for synthesis). The analy
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