Connectionist Representations of Tonal Music: Discovering Musical Patterns by Interpreting Artificial Neural Networks (Michael R. W. Dawson)

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Connectionist Representations of Tonal Music: Discovering Musical Patterns by Interpreting Artificial Neural Networks (Michael R. W. Dawson)

Artificial Neural Networks have only ever been utilized to record the impromptu aspects of music. Michael Dawson, a cognitive scientist, discovered that formal musical properties revealed by artificial neural networks that were trained to make basic tonal music judgments, such as determining the tonic of a scale or the quality of a musical chord, differed significantly from those typically presented in music theory.

For instance, trained artificial neural networks treat notes as if they belong to just three of four different pitch classes, which is a drastically different interpretation of the elements of tonal music than Western music theory, which recognizes twelve special notes or pitch classes.

This book offers many case studies and research findings that deal with issues connected to recognizing scales, and keys, classifying musical chords and learning jazz chord progressions. Its goal is to introduce readers to the application of artificial neural networks in the study of music. Each case study includes a thorough network analysis, illustrating how paying attention to the internal organization of trained networks could significantly impact the research of music cognition.

Ebook Details

About the Authors
Professor of psychology at the University of Alberta, Michael Dawson He is the author of numerous scientific studies as well as the books Connectionism: A Hands-on Approach, Minds and Machines, and Understanding Cognitive Science (2005).
Published Date / Year
(April 1, 2018)
320 pages
eBook Format
HTML and PDF (280 pages)

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