Connectionist Representations of Tonal Music Discovering Musical Patterns by Interpreting Artifical Neural Networks

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  • Engels
  • Paperback
  • 9781771992206
  • 01 april 2018
  • 320 pagina's
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Samenvatting

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 of four different 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 networks is provided for each case study which together demonstrate that focusing on the internal structure of trained networks could yield important contributions to the field of music cognition.

Productspecificaties

Inhoud

Taal
en
Bindwijze
Paperback
Oorspronkelijke releasedatum
01 april 2018
Aantal pagina's
320
Illustraties
Nee

Betrokkenen

Hoofdauteur
Michael R. W. Dawson
Hoofduitgeverij
Au Press

Overige kenmerken

Extra groot lettertype
Nee
Product breedte
152 mm
Product lengte
229 mm
Studieboek
Nee
Verpakking breedte
152 mm
Verpakking hoogte
229 mm
Verpakking lengte
229 mm
Verpakkingsgewicht
0.34 kg

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EAN
9781771992206
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