Evaluating Cognitive Models of Musical Composition

We present a method for the evaluation of creative systems. We deploy a learning-based perceptual model of musical melodic listening in the generation of tonal melodies and evaluate its output quantitatively and objectively, using human judges. Then we show how the system can be enhanced by the application of mathematical methods over data supplied by the judges. The outcome to some extent addresses the criticisms of the experts. We suggest that this is a first step on the road to autonomously learning, introspective, creative systems.

[1]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[2]  C. Krumhansl.  Cognitive Foundations of Musical Pitch , 1990 .

[3]  P. Johnson-Laird.  Jazz Improvization: A Theory at the Computational Level , 1991 .

[4]  Mark L. James,et al.  On the Entropy of Music: An Experiment with Bach Chorale Melodies , 1992 .

[5]  Charles Ames,et al.  Cybernetic composer: an overview , 1992 .

[6]  Ian H. Witten,et al.  Multiple viewpoint systems for music prediction , 1995 .

[7]  Teresa M. Amabile,et al.  Creativity In Context: Update To The Social Psychology Of Creativity , 1996 .

[8]  John G. Cleary,et al.  Unbounded Length Contexts for PPM , 1997 .

[9]  David J. C. Mackay,et al.  Introduction to Monte Carlo Methods , 1998, Learning in Graphical Models.

[10]  Chris Mellish,et al.  Statistical Learning of Harmonic Movement , 1999 .

[11]  Mario Baroni,et al.  Musical Grammar and the Study of Cognitive Processes of Composition , 1999 .

[12]  David Temperley,et al.  What's Key for Key? The Krumhansl-Schmuckler Key-Finding Algorithm Reconsidered , 1999 .

[13]  T. Eerola,et al.  Expectancy-Based Model of Melodic Complexity , 2000 .

[14]  Paul T. von Hippel,et al.  Redefining Pitch Proximity: Tessitura and Mobility as Constraints on Melodic Intervals , 2000 .

[15]  Alan Marsden,et al.  Music, Intelligence and Artificiality , 2000, Readings in Music and Artificial Intelligence.

[16]  Geraint A. Wiggins,et al.  Motivations and Methodologies for Automation of the Compositional Process , 2002 .

[17]  Darrell Conklin,et al.  Music Generation from Statistical Models , 2003 .

[18]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

[19]  Marcus T. Pearce,et al.  The construction and evaluation of statistical models of melodic structure in music perception and composition , 2005 .

[20]  Geraint A. Wiggins,et al.  EXPECTATION IN MELODY: THE INFLUENCE OF CONTEXT AND LEARNING , 2006 .