Abstract
This paper describes the MetaCompose music generator, a compositional, extensible framework for affective music composition. In this context ‘affective’ refers to the music generator’s ability to express emotional information. The main purpose of MetaCompose is to create music in real-time that can express different mood-states, which we achieve through a unique combination of a graph traversal-based chord sequence generator, a search-based melody generator, a pattern-based accompaniment generator, and a theory for mood expression. Melody generation uses a novel evolutionary technique combining FI-2POP with multi-objective optimization. This allows us to explore a Pareto front of diverse solutions that are creatively equivalent under the terms of a multi-criteria objective function. Two quantitative user studies were performed to evaluate the system: one focusing on the music generation technique, and the other that explores valence expression, via the introduction of dissonances. The results of these studies demonstrate (i) that each part of the generation system improves the perceived quality of the music produced, and (ii) how valence expression via dissonance produces the perceived affective state. This system, which can reliably generate affect-expressive music, can subsequently be integrated in any kind of interactive application (e.g., games) to create an adaptive and dynamic soundtrack.
Similar content being viewed by others
Notes
This method is inspired by the “ablation studies” performed by Stanley [87].
References
S. Abrams, D.V. Oppenheim, D. Pazel, J. Wright, et al. Higher-level composition control in music sketcher: modifiers and smart harmony, in Proceedings of the ICMC. Citeseer (1999)
A. Alpern, Techniques for algorithmic composition of music (1995), http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.23.9364&rep=rep1&type=pdf
D. Arsenault, Guitar hero:” not like playing guitar at all. J. Can. Game Stud. Assoc. 1(2), 1–7 (2008)
J.J. Aucouturier, F. Pachet, M. Sandler, the way it sounds: timbre models for analysis and retrieval of music signals. IEEE Trans. Multimed. 7(6), 1028–1035 (2005)
C.P.E. Bach, W.J. Mitchell, W. John, Essay on the True Art of Playing Keyboard Instruments (WW Norton, New York, 1949)
C.D. Batson, L.L. Shaw, K.C. Oleson, Differentiating affect, mood, and emotion: toward functionally based conceptual distinctions, in Emotion (Sage Publications, Inc., Thousand Oaks, CA, 1992), pp. 294–326
C. Beedie, P. Terry, A. Lane, Distinctions between emotion and mood. Cognit. Emot. 19(6), 847–878 (2005)
J. Biles, Genjam: a genetic algorithm for generating jazz solos, in Proceedings of the International Computer Music Conference (International Computer Music Association, 1994), pp. 131–131
D. Birchfield, Generative model for the creation of musical emotion, meaning, and form, in Proceedings of the 2003 ACM SIGMM Workshop on Experiential Telepresence (2003), pp. 99–104
O. Bown, Experiments in modular design for the creative composition of live algorithms. Comput. Music J. 35(3), 73–85 (2011)
C.R. Brewin, Cognitive change processes in psychotherapy. Psychol. Rev. 96(3), 379 (1989)
D. Brown, Mezzo: an adaptive, real-time composition program for game soundtracks, in Proceedings of the AIIDE 2012 Workshop on Musical Metacreation (2012), pp. 68–72
G.C. Bruner, Music, mood, and marketing. J. Mark. 1, 94–104 (1990)
D. Butler, An historical investigation and bibliography of nineteenth century music psychology literature. Ph.D. thesis, Ohio State University (1973)
T. Byron, C. Stevens, Steps and leaps in human memory for melodies: the effect of pitch interval magnitude in a melodic contour discrimination task, in 9th International Conference on Music Perception and Cognition (ICMPC9), Bologna, Italy (Citeseer, 2006)
D. Chafekar, J. Xuan, K. Rasheed, Constrained multi-objective optimization using steady state genetic algorithms, in Genetic and Evolutionary Computation GECCO (Springer, 2003), pp. 813–824
H. Chan, D.A. Ventura, Automatic composition of themed mood pieces, in Proceedings of the International Joint Workshop on Computational Creativity (2008), pp. 19–115
K. Collins, An introduction to procedural music in video games. Contemp. Music Rev. 28(1), 5–15 (2009). doi:10.1080/07494460802663983
D. Cope, Algorithmic music composition, in Patterns of Intuition, ed. by G. Nierhaus (Springer Netherlands, 2015), pp. 405–416. doi:10.1007/978-94-017-9561-6_19
P. Dahlstedt, Autonomous evolution of complete piano pieces and performances, in Proceedings of Music AL Workshop (Citeseer, 2007)
B. De Haas, R.C. Veltkamp, F. Wiering, Tonal pitch step distance: a similarity measure for chord progressions, in ISMIR (2008), pp. 51–56
K. Deb, Multi-objective Optimization Using Evolutionary Algorithms, vol. 16 (Wiley, Hoboken, 2001)
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Trans. Evolut. Comput. 6(2), 182–197 (2002)
K. Deb, A. Pratap, T. Meyarivan, Constrained test problems for multi-objective evolutionary optimization, in Evolutionary Multi-Criterion Optimization (Springer, 2001), pp. 284–298
P. Doornbusch, A brief survey of mapping in algorithmic composition, in Proceedings of the International Computer Music Conference (2002), http://www.academia.edu/download/33447946/A_Brief_Survey_of_Mapping_in_Algorithmic_Composition.pdf
M. Edwards, Algorithmic composition: computational thinking in music. Commun. ACM 54(7), 58–67 (2011). doi:10.1145/1965724.1965742
A.E. Eiben, J. Smith, From evolutionary computation to the evolution of things. Nature 521(7553), 476–482 (2015)
P. Ekman, Are there basic emotions? Psychol. Rev. 99(3), 550–553 (1992). doi:10.1037/0033-295X.99.3.550
P. Ekman, An argument for basic emotions. Cognit. Emot. 6(3–4), 169–200 (1992)
M. Eladhari, R. Nieuwdorp, M. Fridenfalk, The soundtrack of your mind: mind music-adaptive audio for game characters, in Proceedings of Advances in Computer Entertainment Technology (2006)
B. Eno, The ship (2016), http://www.brian-eno.net/
P.R. Farnsworth, The Social Psychology of Music (Dryden, Oxford, 2003), p. 304
A. Gabrielsson, P.N. Juslin, Emotional Expression in Music (Oxford University Press, Oxford, 2003)
J.M. Grey, J.W. Gordon, Perceptual effects of spectral modifications on musical timbres. J. Acoust. Soc. Am. 63(5), 1493–1500 (1978)
R.H. Gundlach, Factors determining the characterization of musical phrases. Am. J. Psychol. 47(4), 624–643 (1935)
K. Hevner, The affective character of the major and minor modes in music. Am. J. Psychol. 47(1), 103–118 (1935)
K. Hevner, Experimental studies of the elements of expression in music. Am. J. Psychol. 48(2), 246–268 (1936)
K. Hevner, The affective value of pitch and tempo in music. Am. J. Psychol. 49(4), 621–630 (1937)
G. Husain, W.F. Thompson, E.G. Schellenberg, Effects of musical tempo and mode on arousal, mood, and spatial abilities. Music Percept. Interdiscip. J. 20(2), 151–171 (2002)
G. Ilie, W.F. Thompson, A comparison of acoustic cues in music and speech for three dimensions of affect. Music Percept. Interdiscip. J. 23(4), 319–330 (2006)
A. Isaacs, T. Ray, W. Smith, Blessings of maintaining infeasible solutions for constrained multi-objective optimization problems, in IEEE Congress on Evolutionary Computation (IEEE, 2008), pp. 2780–2787
F. Jimenez, A.F. Gómez-Skarmeta, G. Sánchez, K. Deb, An evolutionary algorithm for constrained multi-objective optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 1133–1138. IEEE (2002)
P.N. Juslin, S. Liljeström, D. Västfjäll, G. Barradas, A. Silva, An experience sampling study of emotional reactions to music: listener, music, and situation. Emotion 8(5), 668 (2008)
H. Katayose, M. Imai, S. Inokuchi, Sentiment extraction in music, in Proceedings of the 9th International Conference on Pattern Recognition (1988), pp. 1083–1087
S.O. Kimbrough, G.J. Koehler, M. Lu, D.H. Wood, On a feasible-infeasible two-population (fi-2pop) genetic algorithm for constrained optimization: distance tracing and no free lunch. Eur. J. Oper. Res. 190(2), 310–327 (2008)
A. Kirke, E.R. Miranda, A survey of computer systems for expressive music performance. ACM Comput. Surv. 42(1), 3:1–3:41 (2009). doi:10.1145/1592451.1592454
V.J. Konečni, Does music induce emotion? A theoretical and methodological analysis. Psychol. Aesthet. Creat. Arts 2(2), 115 (2008)
A.E. Krause, A.C. North, L.Y. Hewitt, Music-listening in everyday life: devices and choice. Psychol. Music 43(2), 155–170 (2015)
G. Kreutz, U. Ott, D. Teichmann, P. Osawa, D. Vaitl, Using music to induce emotions: influences of musical preference and absorption. Psychol. Music 36(1), 101–126 (2008)
C.L. Krumhansl, An exploratory study of musical emotions and psychophysiology. Can. J. Exp. Psychol. Revue canadienne de psychologie expérimentale 51(4), 336 (1997)
C.G. Lange, W. James, The Emotions (Williams & Wilkins, Baltimore, 1922)
T. Langlois, G. Marques, A music classification method based on timbral features, in ISMIR (2009), pp. 81–86
R.S. Lazarus, Emotion and Adaptation (Oxford University Press, Oxford, 1991)
F. Lerdahl, Tonal pitch space. Music Percept. 5, 315–349 (1988)
J.S. Lerner, D. Keltner, Beyond valence: toward a model of emotion-specific influences on judgement and choice. Cognit. Emot. 14(4), 473–493 (2000)
E. Lindström, P.N. Juslin, R. Bresin, A. Williamon, Expressivity comes from within your soul: a questionnaire study of music students’ perspectives on expressivity. Res. Stud. Music Educ. 20(1), 23–47 (2003)
D. Liu, L. Lu, H.J. Zhang, Automatic mood detection from acoustic music data, in Proceedings of the International Symposium on Music Information Retrieval (2003), pp. 81–87
S.R. Livingstone, A.R. Brown, Dynamic response: real-time adaptation for music emotion, in Proceedings of the 2nd Australasian Conference on Interactive Entertainment (2005), pp. 105–111
R. Loughran, J. McDermott, M. O’Neill, Tonality driven piano compositions with grammatical evolution, in IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2015), pp. 2168–2175
B.A. Martin, The influence of gender on mood effects in advertising. Psychol. Mark. 20(3), 249–273 (2003)
H.P. Martinez, G.N. Yannakakis, J. Hallam, Don’t classify ratings of affect; rank them!. IEEE Trans. Affect. Comput. 5(3), 314–326 (2014)
S.K. Meier, J.L. Briggs, System for real-time music composition and synthesis. US Patent 5,496,962 (1996)
L.B. Meyer, Emotion and Meaning in Music (University of Chicago Press, Chicago, 2008)
K. Miller, Schizophonic performance: guitar hero, rock band, and virtual virtuosity. J. Soc. Am. Music 3(04), 395–429 (2009)
E.R. Miranda, Readings in Music and Artificial Intelligence, vol. 20 (Routledge, London, 2013)
E.R. Miranda, A. Biles, Evolutionary Computer Music (Springer, Berlin, 2007)
K. Monteith, T. Martinez, D. Ventura, Automatic generation of music for inducing emotive response, in Proceedings of the International Conference on Computational Creativity (Citeseer, 2010), pp. 140–149
S. Mugglin, Chord charts and maps, http://mugglinworks.com/chordmaps/chartmaps.htm. Accessed 14 Sept 2015
A.C. North, D.J. Hargreaves, J.J. Hargreaves, Uses of music in everyday life. Music Percept. Interdiscip. J. 22(1), 41–77 (2004)
G. Papadopoulos, G. Wiggins, AI methods for algorithmic composition: a survey, a critical view and future prospects, in AISB Symposium on Musical Creativity, Edinburgh, UK (1999), pp. 110–117
G. Perle, Serial Composition and Atonality: An Introduction to the Music of Schoenberg, Berg, and Webern (Univ of California Press, Berkeley, 1972)
J. Posner, J.A. Russell, B.S. Peterson, The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(03), 715–734 (2005)
M. Puckette, et al. Pure data: another integrated computer music environment, in Proceedings of the Second Intercollege Computer Music Concerts (1996), pp. 37–41
A.P. Rigopulos, E.B. Egozy, Real-time music creation system. US Patent 5,627,335 (1997)
J. Robertson, A. de Quincey, T. Stapleford, G. Wiggins, Real-time music generation for a virtual environment, in Proceedings of ECAI-98 Workshop on AI/Alife and Entertainment (Citeseer, 1998)
R. Rosenthal, D.B. Rubin, A simple, general purpose display of magnitude of experimental effect. J. Educ. Psychol. 74(2), 166 (1982)
J.A. Russell, A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)
E.G. Schellenberg, A.M. Krysciak, R.J. Campbell, Perceiving emotion in melody: interactive effects of pitch and rhythm. Music Percept. Interdiscip. J. 18(2), 155–171 (2000)
K.R. Scherer, A. Schorr, T. Johnstone, Appraisal Processes in Emotion: Theory, Methods, Research (Oxford University Press, Oxford, 2001)
H. Schlosberg, Three dimensions of emotion. Psychol. Rev. 61(2), 81 (1954)
M. Scirea, Mood dependent music generator, in Proceedings of Advances in Computer Entertainment (2013), pp. 626–629
M. Scirea, G.A. Barros, N. Shaker, J. Togelius, Smug: scientific music generator, in Proceedings of the Sixth International Conference on Computational Creativity (2015), p. 204
M. Scirea, M.J. Nelson, J. Togelius, Moody music generator: characterising control parameters using crowdsourcing, in Evolutionary and Biologically Inspired Music, Sound, Art and Design (Springer, 2015), pp. 200–211
M. Scirea, J. Togelius, P. Eklund, S. Risi, Metacompose: a compositional evolutionary music composer, in International Conference on Evolutionary and Biologically Inspired Music and Art (Springer, 2016), pp. 202–217
J.A. Sloboda, S.A. O’Neill, Emotions in everyday listening to music, in Music and Emotion: Theory and Research (Oxford University Press, New York, NY, 2001), pp. 415–429
A. Smaill, G. Wiggins, M. Harris, Hierarchical music representation for composition and analysis. Comput. Humanit. 27(1), 7–17 (1993)
K.O. Stanley, R. Miikkulainen, Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)
R.E. Thayer, The Biopsychology of Mood and Arousal (Oxford University Press, Oxford, 1989)
S.S. Tomkins, Affect Imagery Consciousness: Volume I: The Positive Affects, vol. 1 (Springer, Berlin, 1962)
G.T. Toussaint, et al. The Euclidean algorithm generates traditional musical rhythms, in Proceedings of BRIDGES: Mathematical Connections in Art, Music and Science (2005), pp. 47–56
L.J. Trainor, B.M. Heinmiller, The development of evaluative responses to music: infants prefer to listen to consonance over dissonance. Infant Behav. Dev. 21(1), 77–88 (1998)
D. Watson, A. Tellegen, Toward a consensual structure of mood. Psychol. Bull. 98(2), 219 (1985)
G. Wiggins, M. Harris, A. Smaill, Representing music for analysis and composition. University of Edinburgh, Department of Artificial Intelligence (1990)
R. Wooller, A.R. Brown, E. Miranda, J. Diederich, R. Berry, A framework for comparison of process in algorithmic music systems, in Generative Arts Practice 2005—A Creativity & Cognition Symposium (2005)
W. Wundt, Outlines of Psychology (Springer, Berlin, 1980)
G.N. Yannakakis, J. Togelius, Experience-driven procedural content generation. IEEE Trans. Affect. Comput. 2(3), 147–161 (2011)
E. Zitzler, K. Deb, L. Thiele, Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Scirea, M., Togelius, J., Eklund, P. et al. Affective evolutionary music composition with MetaCompose. Genet Program Evolvable Mach 18, 433–465 (2017). https://doi.org/10.1007/s10710-017-9307-y
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10710-017-9307-y