This article presents the CHAMELEON melodic harmonisation assistant that utilises the principles of conceptual blending theory as a means for the invention of hybrid or novel harmonic idioms and an empirical evaluation of a number of computer-generated melodic harmonisation blends. Melodies originating from various idioms were harmonised either according to the harmonic rules of the original idiom, according to the rules of a different idiom (melody–harmony blends), or by blending idioms, modes and transported versions of the same idiom (harmony–harmony blends). In two similar experimental set ups, the task of the listeners was to i) perform idiom, mode or type of chromaticism classification, ii) report their preference, and iii) rate the degree of expectancy characterising each harmonisation. The results show that harmonic blending (either melody–harmony or harmony–harmony) influences the identification of idiom, mode and type of chromaticism. This suggests that the harmonic blending system has indeed succeeded in producing perceivable blends under various conditions that were unexpected and also equally preferred compared to non-blends.
AmesC. (1992). Quantifying musical merit. Journal of New Music Research, 21(1), 53–93.
2.
AntovicM.StamenkovicD.FigarV. (2016). Association of meaning in program music: On denotation, inherence, and onomatopoeia. Music Perception, 34, 243–248.
3.
BodenM. A. (2004). The creative mind: Myths and mechanisms (2nd ed.). London, UK: Routledge.
4.
CambouropoulosE. (2015). The harmonic musical surface and two novel chord representation schemes. In MeredithD. (Ed.), Computational music analysis (pp. 31–56). Berlin, Germany: Springer.
5.
CambouropoulosE.Kaliakatsos-PapakostasM.TsougrasC. (2014, September). An idiom-independent representation of chords for computational music analysis and generation. In GeorgakiA.KouroupetroglouG. (Eds.), Proceedings of the joint 11th Sound and Music Computing Conference (SMC) and 40th International Computer Music Conference (ICMC-SMC 2014) (pp. 1002–1009). Athens, Greece. Retrieved from http://speech.di.uoa.gr/ICMC-SMC-2014/images/VOL_2/1002.pdf
6.
ColtonS. (2008, March). Creativity versus the perception of creativity in computational systems. In Proceedings of AAAI Symposium on Creative Systems (pp. 14–20). Stanford, CA.
7.
ColtonS.CharnleyJ.PeaseA. (2011, April). Computational creativity theory: The FACE and IDEA descriptive models. In Proceedings of the 2nd International Conference on Computational Creativity (pp. 90–95). Mexico City, Mexico.
8.
EppeM.ConfalonieriR.MacleanE.Kaliakatsos-PapakostasM.CambouropoulosE.SchorlemmerM.CodescuM.KühnbergerK.-U. (2015). Computational invention of cadences and chord progressions by conceptual chord-blending. In International Joint Conference on Artificial Intelligence (IJCAI) 2015.
9.
FauconnierG.TurnerM. (2003). The way we think: Conceptual blending and the mind’s hidden complexities (Rep. ed.). New York, NY: Basic Books.
10.
GoguenJ. A. (2006). Mathematical models of cognitive space and time. In AndlerD.OgawaY.OkadaM.WatanabeS. (Eds.), Reasoning and cognition: Interdisciplinary conference series on reasoning studies (Vol. 2, pp. 125–128). Tokyo, Japan: Keio University Press.
11.
Granroth-WildingM.SteedmanM. (2014). A robust parser-interpreter for jazz chord sequences. Journal of New Music Research, 43, 355–374.
12.
JordanousA. (2012). Evaluating computational creativity: A standardised procedure for evaluating creative systems and its application (Unpublished doctoral dissertation). University of Sussex, Brighton, UK.
13.
JordanousA. (2016). Four PPPPerspectives on computational creativity in theory and in practice. Connection Science, 28, 194–216.
14.
Kaliakatsos-PapakostasM.CambouropoulosE. (2014, September). Probabilistic harmonization with fixed intermediate chord constraints. In GeorgakiA.KouroupetroglouG. (Eds.), Proceedings of the joint 11th Sound and Music Computing Conference (SMC) and 40th International Computer Music Conference (ICMC-SMC 2014) (pp. 1083–1090). Athens, Greece. Retrieved from http://speech.di.uoa.gr/ICMC-SMC-2014/images/VOL_2/1083.pdf
15.
Kaliakatsos-PapakostasM.MakrisD.TsougrasC.CambouropoulosE. (2016). Learning and creating novel harmonies in diverse musical idioms: An adaptive modular melodic harmonisation system. Journal of Creative Music Systems, 1(1). Retrieved from http://openmusiclibrary.org/article/982966
16.
Kaliakatsos-PapakostasM.QueirozM.TsougrasC.CambouropoulosE. (2017). Conceptual blending of harmonic spaces for creative melodic harmonisation. In press.
17.
Kaliakatsos-PapakostasM.ZacharakisA.TsougrasC.CambouropoulosE. (2015, October). Evaluating the general chord type representation in tonal music and organising GCT chord labels in functional chord categories. In MüllerM.WieringF. (Eds.), Proceedings of the International Society of Music Information Retrieval Conference (ISMIR 2015) (pp. 427– 433). Malaga, Spain.
18.
KoopsH. V.MagalhãesJ. P.de HaasW. B. (2013). A functional approach to automatic melody harmonisation. Proceedings of the First ACM SIGPLAN Workshop on Functional Art, Music, Modelling and Design (pp 47–58). New York, NY: ACM.
19.
MacKinnonD. W. (1970). Creativity: A multi-faceted phenomenon. In RoslanskyJ. D. (Ed.), Creativity: A discussion at the nobel conference (pp. 17–32). Amsterdam, the Netherlands: North-Holland Publishing Company.
20.
MargulisE. H.BeattyA. P. (2008). Musical style, psychoaesthetics, and prospects for entropy as an analytic tool. Computer Music Journal, 32, 64–78.
21.
MakrisD.Kaliakatsos-PapakostasM.CambouropoulosE. (2015). A probabilistic approach to determining bass voice leading in melodic harmonisation. Proceedings of the 5th International Conference on Mathematics and Computation in Music (MCM 2015). Berlin, Germany: Springer.
22.
MayerR. E. (1999). Fifty years of creativity research. In SternbergR. J. (Ed.), Handbook of creativity (pp. 449–460). Cambridge, UK: Cambridge University Press.
23.
MoorsJ. J. A. (1986). The meaning of kurtosis: Darlington reexamined. The American Statistician, 40, 283–284.
24.
PearceM.WigginsG. (2001, March). Towards a framework for the evaluation of machine compositions. Proceedings of the AISB’01 Symposium on Artificial Intelligence and Creativity in the Arts and Sciences (pp. 22–32). York, UK.
25.
RitchieG. (2007). Some empirical criteria for attributing creativity to a computer program. Minds and Machines, 17, 67–99.
26.
RhodesM. (1961). An analysis of creativity. Phi Delta Kappan, 42, 305–310.
27.
RohrmeierM. (2011). Towards a generative syntax of tonal harmony. Journal of Mathematics and Music, 5, 35–53.
WigginsG. A. (2006). A preliminary framework for description, analysis and comparison of creative systems. Knowledge-Based Systems, 19, 449–458.
30.
ZacharakisA.Kaliakatsos-PapakostasM.CambouropoulosE. (2015). Conceptual blending in music cadences: A formal model and subjective evaluation. In MüllerM.WieringF. (Eds.), Proceedings of the International Society of Music Information Retrieval Conference (ISMIR 2015) (pp. 141–147). Malaga, Spain.
31.
ZacharakisA.Kaliakatsos-PapakostasM.TsougrasC.CambouropoulosE. (in press). Creating musical cadences via conceptual blending: Empirical evaluation and enhancement of a formal model. Music Perception.
32.
ZbikowskiL. M. (2002). Conceptualizing music: Cognitive structure, theory, and analysis. Oxford, UK: Oxford University Press.
33.
ZbikowskiL. M. (2006). The cognitive tango. In TurnerM. (Ed.), The artful mind: Cognitive science and the riddle of human creativity (pp. 115–137). Oxford, UK: Oxford University Press.
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