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Piece for Smartphone Orchestra

Introduction

Piece for Smartphone Orchestra is a sound work for any number of players. It is a manifestation of a complex systems view of the artwork. Form is a dynamic structure that depends on the interactions and choices of the performers. A text score directs the actions of the players. Also, by means of a graphic shape, it offers a vague idea about the form of the piece. The duration of the performance is indeterminate.

Each player uses a smartphone to control a software synthesizer. The interface includes three buttons. It utilizes smartphone sensors to map device rotation angles to amplitude and frequency deviation. Each button starts/stops a tone. Tone frequencies center around 1244.507 Hz, 2489.014 Hz and 4978.031 Hz. Rotation of the device modifies the frequency of each button to an interval with radius 100 cents around the center frequency. The piece may be perceived as a potential aural game on beat tones and otoacoustic emission effects1. It is an attempt to create with very little material, a varying iridescent soundscape.

Ideas that relate to systems science and were influnential to Piece for Smartphone Orchestra should be traced in the writings of Horacio Vaggione23 and Agostino DiScipio45. Also, in the same vein should be regarded work that builds upon artificial swarms 678 and artificial ecosystems91011. These authors reify the concept of a system to a computer simulation or a computational process. As such, most of the elements of the system and the interactions among them can be defined and manipulated by computer algorithms. In Piece for Smartphone Orchestra the interacting elements are the performers. In order to steer the performance towards an aesthetic goal we choose to shape the interactions between the performers and the performers with the environment by using text and graphics. Our approach is similar to the text scores of Pauline Oliveros12. The players react to the global sound and act according to the directions given in the text.

We assume that the interpretation of the score activates a network of mental spaces to each performer. According to Fauconnier "mental spaces are very partial assemblies constructed as we think and talk for purposes of local understanding and action"13. They are connected to each other and are being modified during the rehearsals and performances of the piece. This web of mental spaces represents an abstract idea about the form of Piece for Smartphone Orchestra.

Complex systems

Systems science is an umbrella term for the study of aggregations of interacting elements. It encompasses ideas and methodologies from dynamical systems, cybernetics and chaos theory. A complex system is "one made up of a large number of parts that interact in a nonsimple way"14. Holland15 refers to these interacting elements as agents. Localized interactions among the agents may lead to aggregate patterns of behaviour. This phenomenon is called emergence16.

We are interested in complex systems where the agents adapt their behaviour based on experience. Such systems are called complex adaptive systems. Levin17 defines a complex adaptive system by three properties: (1) diversity and individuality of the agents, (2) localized interactions among them, and (3) an autonomous process that uses the outcomes of those interactions to select a subset of the agents for replication or enhancement.

Piece for Smartphone Orchestra as a complex system

The agents of the system are the performers of the piece. The state of each agent is defined by a web of mental spaces. These form and modify during the rehearsals and the performances of the piece. The text score offers the clues to construct the internal model18 of the agent.

The complex system of Piece for Smartphone Orchestra should be regarded as a process that starts with the rehearsals. Agents interact by means of communication. Communication is a strategic phenomenon in the sense that shapes agent's anticipation, action and reaction19. It encompasses sound, gestures and any related conversation between the players during the preparation of the piece. Closely related is the system property of flows20. This is an evolving web of interactions that "reflects changing adaptions as time elapses and experience accumulates"21.

Siegenfeld and Bar-Yam22 regard the complexity of a system to depend on the number of possible behaviours that the system can exhibit. Behaviour depends on scale. We are interested in the acoustic behaviour of the system during a performance. For scale we assume spatial scale.

On one end there is the whole performance space. At this level Piece for Smartphone Orchestra appears as an arc-like structure that unfolds during the evolution of the piece. Tones, beats and (possible) auditory distortion products come and disappear as frequencies, frequency deviations and amplitudes follow the arc-like structure. These beats and auditory distorion products can be viewed as global emergent properties of the system.

At the other end we have the space around an individual agent. At this level, behaviour is set by the directions in the score. This could be a single sine tone or silence.

Between these two extremes we can define potential areas inside the performance space were certain sounds can only be heared. These are not fixed and may overlap. These localities of sound depend on the distance between the agents, the amplitudes and frequencies of the playing tones, as well as, the direction or the movements of the players. Localities of sound may encompass single tones, beats or even auditory distortion products. They should be regarded as local emergent properties of the system.

The last few paragraphs suggest a multi-scale approach to the sound structure of the piece. Different scales are interdependent and form the input information of the agents. This information is different for each agent. Taken together with player's action form a feedback loop.

Footnotes

  1. Kendall GS, Haworth C, Cádiz RF. "Sound Synthesis with Auditory Distortion Products". Computer Music Journal 38, 4 (2014):5–23. ↩

  2. Vaggione, Horacio. “Some Ontological Remarks about Music Composition Processes.” Computer Music Journal 25, no. 1 (2001): 54–61. https://doi.org/10.1162/014892601300126115 ↩

  3. Vaggione, Horacio. “Articulating Microtime.” Computer Music Journal 20, no. 2 (1996): 33. https://doi.org/10.2307/3681329 ↩

  4. Di Scipio, Agostino. “‘Sound Is the Interface’: From Interactive to Ecosystemic Signal Processing.” Organised Sound 8, no. 3 (2003): 269–77. https://doi.org/10.1017/s1355771803000244 ↩

  5. Di Scipio, Agostino. “Listening to Yourself through the Otherself: On Background Noise Study and Other Works.” Organised Sound 16, no. 2 (2011): 97–108. https://doi.org/10.1017/s1355771811000033 ↩

  6. Blackwell, Tim. “Swarming and Music.” Evolutionary Computer Music, (2007): 194–217. https://doi.org/10.1007/978-1-84628-600-1_9 ↩

  7. Blackwell, T.M., and P. Bentley. “Improvised Music with Swarms.” Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (2002): 1462 - 1467. https://doi.org/10.1109/cec.2002.1004458 ↩

  8. Blackwell, Tim, and Michael Young. “Self-Organised Music.” Organised Sound 9, no. 2 (2004): 123–36. https://doi.org/10.1017/s1355771804000214 ↩

  9. Bown, Oliver, and Jon McCormack. “Taming Nature: Tapping the Creative Potential of Ecosystem Models in the Arts.” Digital Creativity 21, no. 4 (2010): 215–31. https://doi.org/10.1080/14626268.2011.550029 ↩

  10. McCormack, Jon. “Evolving Sonic Ecosystems.” Kybernetes 32, no. 1/2 (2003): 184–202. https://doi.org/10.1108/03684920310452409 ↩

  11. An overview of biologically inspired music systems can be found in:
    Eldridge, Alice, and Oliver Bown. “Biologically Inspired and Agent-Based Algorithms for Music.” Oxford Handbooks Online, 2018. https://doi.org/10.1093/oxfordhb/9780190226992.013.18 ↩

  12. Oliveros P, Pertl B. Anthology of text scores. New York: Deep Listening Publications; 2013. ↩

  13. Fauconnier, Gilles. “Mental Spaces.” Oxford Handbooks Online, 2010. https://doi.org/10.1093/oxfordhb/9780199738632.013.0014 ↩

  14. Simon H.A. The Architecture of Complexity. In: Facets of Systems Science. International Federation for Systems Research International Series on Systems Science and Engineering, vol 7. Springer, Boston, MA; 1991. https://doi.org/10.1007/978-1-4899-0718-9_31 ↩

  15. See chapter 1 in:
    Holland JH. Hidden order: how adaptation builds complexity. Cambridge, MA: Perseus Books; 2003. ↩

  16. See chapter 4 in:
    Miller JH, Page SE. Complex adaptive systems: an introduction to computational models of social life. Princeton, NJ: Princeton University Press; 2007. ↩

  17. Levin, S. (2002). Complex adaptive systems: Exploring the known, the unknown and the unknowable. Bulletin of the American Mathematical Society, 40(1), 3–19. https://doi.org/10.1090/s0273-0979-02-00965-5 ↩

  18. Holland, Hidden order: how adaption builds complexity, chapter 1. ↩

  19. Miller and Page, Complex adaptive systems: an introduction to computational models of social life, chapter 10. ↩

  20. Holland, Hidden order: how adaption builds complexity, chapter 1. ↩

  21. Ibid. ↩

  22. Siegenfeld, Alexander F., and Yaneer Bar-Yam. “An Introduction to Complex Systems Science and Its Applications.” Complexity (2020): 1–16. https://doi.org/10.1155/2020/6105872 ↩