Conceptual Blending in Music Cadences: A Formal Model and Subjective Evaluation

Conceptual blending is a cognitive theory whereby elements from diverse, but structurally-related, mental spaces are ‘blended’ giving rise to new conceptual spaces. This study focuses on structural blending utilising an algorithmic formalisation for conceptual blending applied to harmonic concepts. More specifically, it investigates the ability of the system to produce meaningful blends between harmonic cadences, which arguably constitute the most fundamental harmonic concept. The system creates a variety of blends combining elements of the penultimate chords of two input cadences and it further estimates the expected relationships between the produced blends. Then, a preliminary subjective evaluation of the proposed blending system is presented. A pairwise dissimilarity listening test was conducted using original and blended cadences as stimuli. Subsequent multidimensional scaling analysis produced spatial configurations for both behavioural data and dissimilarity estimations by the algorithm. Comparison of the two configurations showed that the system is capable of making fair predictions of the perceived dissimilarities between the blended cadences. This implies that this conceptual blending approach is able to create perceptually meaningful blends based on self-evaluation of its outcome.

[1]  Lawrence M. Zbikowski Conceptualizing Music: Cognitive Structure, Theory, and Analysis , 2002 .

[2]  Kai-Uwe Kühnberger,et al.  Computational Invention of Cadences and Chord Progressions by Conceptual Chord-Blending , 2015, IJCAI.

[3]  M. Boden The creative mind : myths & mechanisms , 1991 .

[4]  Geraint A. Wiggins,et al.  Towards A Framework for the Evaluation of Machine Compositions , 2001 .

[5]  Forrest W. Young Nonmetric multidimensional scaling: Recovery of metric information , 1970 .

[6]  Peter D. Mosses,et al.  Casl Reference Manual: The Complete Documentation Of The Common Algebraic Specification Language (LECTURE NOTES IN COMPUTER SCIENCE) , 2004 .

[7]  Santiago Ontañón,et al.  Amalgams: A Formal Approach for Combining Multiple Case Solutions , 2010, ICCBR.

[8]  Geraint A. Wiggins,et al.  Evaluating Cognitive Models of Musical Composition , 2007 .

[9]  Paul Sambre,et al.  Gilles Fauconnier & Mark Turner, " The way we think: conceptual blending and the mind's hidden complexities" , 2002 .

[10]  Nicholas Cook Theorizing Musical Meaning , 2001 .

[11]  Kai-Uwe Kühnberger,et al.  COINVENT: Towards a Computational Concept Invention Theory , 2014, ICCC.

[12]  L. Tucker A METHOD FOR SYNTHESIS OF FACTOR ANALYSIS STUDIES , 1951 .

[13]  J. Berge,et al.  Tucker's congruence coefficient as a meaningful index of factor similarity. , 2006 .

[14]  Maximos A. Kaliakatsos-Papakostas,et al.  An Idiom-independent Representation of Chords for Computational Music Analysis and Generation , 2014, ICMC.

[15]  Joshua D. Reiss,et al.  An Interlanguage Unification of Musical Timbre: Bridging Semantic, Perceptual, and Acoustic Dimensions , 2015 .

[16]  R. Shepard Metric structures in ordinal data , 1966 .

[17]  G. Fauconnier,et al.  The Way We Think: Conceptual Blending and the Mind''s Hidden Complexities. Basic Books , 2002 .

[18]  Anna K. Jordanous Evaluating computational creativity : a standardised procedure for evaluating creative systems and its application , 2013 .

[19]  Robin C. Laney,et al.  Developing and evaluating computational models of musical style , 2015, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[20]  J. Goguen Mathematical Models of Cognitive Space and Time , 2006 .

[21]  Kai-Uwe Kühnberger,et al.  Concept invention and music: Creating novel harmonies via conceptual blending , 2014 .

[22]  Geraint A. Wiggins,et al.  A preliminary framework for description, analysis and comparison of creative systems , 2006, Knowl. Based Syst..