Connectionist Representations of Tonal Music

Discovering Musical Patterns by Interpreting Artifical Neural Networks

Nonfiction, Entertainment, Music, Theory & Criticism, Theory, Health & Well Being, Psychology, Cognitive Psychology
Cover of the book Connectionist Representations of Tonal Music by Michael R. W. Dawson, Athabasca University Press
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Author: Michael R. W. Dawson ISBN: 9781771992220
Publisher: Athabasca University Press Publication: March 14, 2018
Imprint: AU Press Language: English
Author: Michael R. W. Dawson
ISBN: 9781771992220
Publisher: Athabasca University Press
Publication: March 14, 2018
Imprint: AU Press
Language: English

Previously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks revealed formal musical properties that differ dramatically from those typically presented in music theory. For example, where Western music theory identifies twelve distinct notes or pitch-classes, trained artificial neural networks treat notes as if they belong to only three or four pitch-classes, a wildly different interpretation of the components of tonal music. Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of the internal structure of trained networks could yield important contributions to the field of music cognition.

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Previously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks revealed formal musical properties that differ dramatically from those typically presented in music theory. For example, where Western music theory identifies twelve distinct notes or pitch-classes, trained artificial neural networks treat notes as if they belong to only three or four pitch-classes, a wildly different interpretation of the components of tonal music. Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of the internal structure of trained networks could yield important contributions to the field of music cognition.

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