David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Minds and Machines 2 (4):379-400 (1992)
Current artificial neural network or connectionist models of music cognition embody feature-extraction and feature-weighting principles. This paper reports two experiments which seek evidence for similar processes mediating recognition of short musical compositions by musically trained and untrained listeners. The experiments are cast within a pattern recognition framework based on the vision-audition analogue wherein music is considered an auditory pattern consisting of local and global features. Local features such as inter-note interval, and global features such as melodic contour, are derived from a two-dimensional matrix in which music is represented as a series of frequencies plotted over time.Manipulation of inter-note interval affected accuracy and reaction time measures in a discrimination task, whereas the same variables were affected by manipulation of melodic contour in a classification task. Musical training is thought of as a form of practice in musical pattern recognition and, as predicted, accuracy and reaction time measures of musically trained subjects were significantly better than those of untrained subjects. Given the evidence for feature-extraction and weighting processes in music recognition tasks, two connectionist models are discussed. The first is a single-layer perceptron which has been trained to discriminate between compositions according to inter-note interval. A second network, using the back-propagation algorithm and sequential input of patterns, is also discussed.
|Keywords||Music recognition connectionism neural networks pattern recognition features computer simulation|
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References found in this work BETA
Paul Smolensky (1988). On the Proper Treatment of Connectionism. Behavioral and Brain Sciences 11 (1):1-23.
Jeffrey L. Elman (1990). Finding Structure in Time. Cognitive Science 14 (2):179-211.
Andy Clark (1991). Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. Cambridge: MIT Press.
R. J. Wherry (1938). Orders for the Presentation of Pairs in the Method of Paired Comparisons. Journal of Experimental Psychology 23 (6):651.
Citations of this work BETA
Martin Rohrmeier & Patrick Rebuschat (2012). Implicit Learning and Acquisition of Music. Topics in Cognitive Science 4 (4):525-553.
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