Affectively-Driven Algorithmic Composition (AAC)

Chapter
Part of the International Series on Computer Entertainment and Media Technology book series (ISCEMT)

Abstract

This chapter introduces a working concept which a number of subsequent chapters will rely upon: Affectively-Driven Algorithmic Composition (or AAC). The reader should note that this is not related to perceptual data compression as in the Apple Lossless file format AAC. Instead it refers to a specific subset of interdisciplinary practices marrying sound design opportunities with emotional intent; a paradigm which is ideally suited to modern video game soundtracking practice. This chapter builds upon initial work reported in the ACM Computers in Entertainment journal (though in an online article, not a specific journal edition), in 2017 (Williams et al. 2017).

References

  1. Bown, O., Lexer, S.: Continuous-time recurrent neural networks for generative and interactive musical performance. In: Applications of Evolutionary Computing, pp. 652–663. Springer, Cham (2006)CrossRefGoogle Scholar
  2. Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry. 25, 49–59 (1994)CrossRefGoogle Scholar
  3. Bresin, R.: Artificial neural networks based models for automatic performance of musical scores. J. New Music Res. 27, 239–270 (1998)CrossRefGoogle Scholar
  4. Brown, E., Cairns P.: A grounded investigation of game immersion. In: CHI’04 Extended Abstracts on Human Factors in Computing Systems, pp. 1297–1300. ACM, New York (2004)Google Scholar
  5. Carpenter, G.A., Grossberg, S., et al.: A self-organizing neural network for supervised learning, recognition, and prediction. IEEE Commun. Mag. 30, 38–49 (1992)CrossRefGoogle Scholar
  6. Casey, M.: General sound classification and similarity in MPEG-7. Organ. Sound. 6, 153 (2001).  https://doi.org/10.1017/S1355771801002126 CrossRefGoogle Scholar
  7. Dahlstedt, P.: A MutaSynth in parameter space: interactive composition through evolution. Organ. Sound. 6, 121 (2001).  https://doi.org/10.1017/S1355771801002084 CrossRefGoogle Scholar
  8. Edwards, M.: Algorithmic composition: computational thinking in music. Commun. ACM. 54, 58–67 (2011)CrossRefGoogle Scholar
  9. Eigenfeldt, A.: Real-time composition as performance ecosystem. Organ. Sound. 16, 145–153 (2011).  https://doi.org/10.1017/S1355771811000094 CrossRefGoogle Scholar
  10. Kirke, A., Miranda, E.R.: A survey of computer systems for expressive music performance. ACM Comput. Surv. 42, 1–41 (2009).  https://doi.org/10.1145/1592451.1592454 CrossRefGoogle Scholar
  11. Rowe, R.: Interactive Music Systems: Machine Listening and Composing. MIT Press, Cambridge (1992)Google Scholar
  12. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161 (1980)CrossRefGoogle Scholar
  13. Visell, Y.: Spontaneous organisation, pattern models, and music. Organ. Sound. 9, 151 (2004).  https://doi.org/10.1017/S1355771804000238 CrossRefGoogle Scholar
  14. Williamon, A., Davidson, J.W.: Exploring co-performer communication. Music. Sci. 6, 53–72 (2002)CrossRefGoogle Scholar
  15. Williams, D., Kirke, A., Miranda, E., Daly, I., Hallowell, J., Weaver, J., Malik, A., Roesch, E., Hwang, F., Nasuto, S.: Investigating perceived emotional correlates of rhythmic density in algorithmic music composition. ACM Trans. Appl. Percept. 12, 8 (2015)CrossRefGoogle Scholar
  16. Williams, D., Kirke, A., Miranda, E., Daly, I., Hwang, F., Weaver, J., Nasuto, S.: Affective calibration of musical feature sets in an emotionally intelligent music composition system. ACM Trans. Appl. Percept. 14, 1–13 (2017).  https://doi.org/10.1145/3059005 CrossRefGoogle Scholar
  17. Williams, D., Kirke, A., Miranda, E.R., Roesch, E., Daly, I., Nasuto, S.: Investigating affect in algorithmic composition systems. Psychol. Music. 43, 831 (2014).  https://doi.org/10.1177/0305735614543282 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Digital Creativity LabsUniversity of YorkYorkUK

Personalised recommendations