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Artificial Neural Networks that Classify Musical Chords

Artificial Neural Networks that Classify Musical Chords

Vanessa Yaremchuk, Michael R.W. Dawson
Copyright: © 2008 |Volume: 2 |Issue: 3 |Pages: 9
ISSN: 1557-3958|EISSN: 1557-3966|ISSN: 1557-3958|EISBN13: 9781615201938|EISSN: 1557-3966|DOI: 10.4018/jcini.2008070102
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MLA

Yaremchuk, Vanessa, and Michael R.W. Dawson. "Artificial Neural Networks that Classify Musical Chords." IJCINI vol.2, no.3 2008: pp.22-30. http://doi.org/10.4018/jcini.2008070102

APA

Yaremchuk, V. & Dawson, M. R. (2008). Artificial Neural Networks that Classify Musical Chords. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2(3), 22-30. http://doi.org/10.4018/jcini.2008070102

Chicago

Yaremchuk, Vanessa, and Michael R.W. Dawson. "Artificial Neural Networks that Classify Musical Chords," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 2, no.3: 22-30. http://doi.org/10.4018/jcini.2008070102

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Abstract

An artificial neural network was trained to classify musical chords into four categories—major, dominant seventh, minor, or diminished seventh—independent of musical key. After training, the internal structure of the network was analyzed in order to determine the representations that the network was using to classify chords. It was found that the first layer of connection weights in the network converted the local representations of input notes into distributed representations that could be described in musical terms as circles of major thirds and on circles of major seconds. Hidden units then were able to use this representation to organize stimuli geometrically into a simple space that was easily partitioned by output units to classify the stimuli. This illustrates one potential contribution of artificial neural networks to cognitive informatics: the discovery of novel forms of representation in systems that can accomplish intelligent tasks.

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