Abstract
We characterise the expressive effects of a music generator capable of varying its moods through two control parameters. The two control parameters were constructed on the basis of existing work on valence and arousal in music, and intended to provide control over those two mood factors. In this paper we conduct a listener study to determine how people actually perceive the various moods the generator can produce. Rather than directly attempting to validate that our two control parameters represent arousal and valence, instead we conduct an open-ended study to crowd-source labels characterising different parts of this two-dimensional control space. Our aim is to characterise perception of the generator’s expressive space, without constraining listeners’ responses to labels specifically aimed at validating the original arousal/valence motivation. Subjects were asked to listen to clips of generated music over the Internet, and to describe the moods with free-text labels. We find that the arousal parameter does roughly map to perceived arousal, but that the nominal “valence” parameter has strong interaction with the arousal parameter, and produces different effects in different parts of the control space. We believe that the characterisation methodology described here is general and could be used to map the expressive range of other parameterisable generators.
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Notes
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Since we make no assumption about the distribution of data, we can’t use the standard error as a basis for a confidence interval. Nonetheless, it is useful as a proxy for how well we can localise a label in the arousal/valence space, relative to other labels in our data set.
- 2.
References
Yannakakis, G.N., Togelius, J.: Experience-driven procedural content generation. IEEE Trans. Affect. Comput. 2(3), 147–161 (2011)
Wooller, R., Brown, A.R., Miranda, E., Diederich, J., Berry, R.: A framework for comparison of process in algorithmic music systems. In: Generative Arts Practice 2005 – A Creativity & Cognition Symposium (2005)
Birchfield, D.: Generative model for the creation of musical emotion, meaning, and form. In: Proceedings of the 2003 ACM SIGMM Workshop on Experiential Telepresence, pp. 99–104 (2003)
Eladhari, M., Nieuwdorp, R., Fridenfalk, M.: The soundtrack of your mind: mind music-adaptive audio for game characters. In: Proceedings of Advances in Computer Entertainment Technology (2006)
Livingstone, S.R., Brown, A.R.: Dynamic response: Real-time adaptation for music emotion. In: Proceedings of the 2nd Australasian Conference on Interactive Entertainment, pp. 105–111 (2005)
Lazarus, R.S.: Emotion and Adaptation. Oxford University Press, New York (1991)
Brewin, C.R.: Cognitive change processes in psychotherapy. Psychol. Rev. 96(3), 379 (1989)
Lerner, J.S., Keltner, D.: Beyond valence: toward a model of emotion-specific influences on judgement and choice. Cogn. Emot. 14(4), 473–493 (2000)
Martin, B.A.: The influence of gender on mood effects in advertising. Psychol. Mark. 20(3), 249–273 (2003)
Batson, C.D., Shaw, L.L., Oleson, K.C.: Differentiating affect, mood, and emotion: Toward functionally based conceptual distinctions (1992)
Beedie, C., Terry, P., Lane, A.: Distinctions between emotion and mood. Cogn. Emot. 19(6), 847–878 (2005)
Katayose, H., Imai, M., Inokuchi, S.: Sentiment extraction in music. In: Proceedings of the 9th International Conference on Pattern Recognition, pp. 1083–1087 (1988)
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)
Thayer, R.E.: The Biopsychology of Mood and Arousal. Oxford University Press, New York (1989)
Kreutz, G., Ott, U., Teichmann, D., Osawa, P., Vaitl, D.: Using music to induce emotions: influences of musical preference and absorption. Psychol. Music 36(1), 101–126 (2008)
Lindström, E., Juslin, P.N., Bresin, R., Williamon, A.: “Expressivity comes from within your soul”: a questionnaire study of music students’ perspectives on expressivity. Res. Stud. Music Educ. 20(1), 23–47 (2003)
Liu, D., Lu, L., Zhang, H.J.: Automatic mood detection from acoustic music data. In: Proceedings of the International Symposium on Music Information Retrieval, pp. 81–7 (2003)
Scirea, M.: Mood dependent music generator. In: Reidsma, D., Katayose, H., Nijholt, A. (eds.) ACE 2013. LNCS, vol. 8253, pp. 626–629. Springer, Heidelberg (2013)
Aucouturier, J.J., Pachet, F., Sandler, M.: “the way it sounds”: timbre models for analysis and retrieval of music signals. IEEE Trans. Multimedia 7(6), 1028–1035 (2005)
Bach, C.P.E., Mitchell, W.J., John, W.: Essay on the True Art of Playing Keyboard Instruments. WW Norton, New York (1949)
Meyer, L.B.: Emotion and Meaning in Music. University of Chicago Press, Chicago (2008)
Trainor, L.J., Heinmiller, B.M.: The development of evaluative responses to music: infants prefer to listen to consonance over dissonance. Infant Behav. Dev. 21(1), 77–88 (1998)
Scirea, M., Cheong, Y.G., Bae, B.C.: Mood expression in real-time computer generated music using pure data. In: Proceedings of the International Conference on Music Perception and Cognition (2014)
Livingstone, S.R., Brown, A.R., Muhlberger, R.: Influencing the perceived emotions of music with intent. In: Proceedings of the Third International Conference on Generative Systems in the Electronic Arts (2005)
Shaker, N., Yannakakis, G.N., Togelius, J.: Towards automatic personalized content generation for platform games. In: Proceedings of the 2010 Conference on Artificial Intelligence and Interactive Digital Entertainment (2010)
Scirea, M., Cheong, Y.G., Bae, B.C., Nelson, M.: Evaluating musical foreshadowing of videogame narrative experiences. In: Proceedings of Audio Mostly (2014)
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Scirea, M., Nelson, M.J., Togelius, J. (2015). Moody Music Generator: Characterising Control Parameters Using Crowdsourcing. In: Johnson, C., Carballal, A., Correia, J. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2015. Lecture Notes in Computer Science(), vol 9027. Springer, Cham. https://doi.org/10.1007/978-3-319-16498-4_18
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