Connectionist Representations of Tonal Music

Discovering Musical Patterns by Interpreting Artificial Neural Networks

reviously, 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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CC BY-NC-SAThis book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Connectionist Representations of Tonal Music for free.

Title
Connectionist Representations of Tonal Music
Subtitle
Discovering Musical Patterns by Interpreting Artificial Neural Networks
Publisher
Author(s)
Published
2018/4/24
Edition
1
Format
eBook (pdf, epub, mobi)
Pages
312
Language
English
ISBN-10
1771992204
ISBN-13
9781771992206
License
CC BY-NC-SA
Book Homepage
Free eBook, Errata, Code, Solutions, etc.
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