Computational Synthesis and Creative Systems

Deep Learning Techniques for Music Generation

Autoren: Briot, Jean-Pierre, Hadjeres, Gaëtan, Pachet, François-David

Vorschau
  • Authors' analysis based on five dimensions: objective, representation, architecture, challenge, and strategy
    Important application of deep learning, for AI researchers and composers
    Research was conducted within the EU Flow Machines project

Dieses Buch kaufen

eBook 93,08 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-70163-9
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: PDF
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 117,69 €
Preis für Deutschland (Brutto)
Über dieses Buch

This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure.

The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.

Inhaltsverzeichnis (8 Kapitel)

Inhaltsverzeichnis (8 Kapitel)

Dieses Buch kaufen

eBook 93,08 €
Preis für Deutschland (Brutto)
  • ISBN 978-3-319-70163-9
  • Versehen mit digitalem Wasserzeichen, DRM-frei
  • Erhältliche Formate: PDF
  • eBooks sind auf allen Endgeräten nutzbar
  • Sofortiger eBook Download nach Kauf
Hardcover 117,69 €
Preis für Deutschland (Brutto)
Loading...

Wir empfehlen

Loading...

Bibliografische Information

Bibliographic Information
Buchtitel
Deep Learning Techniques for Music Generation
Autoren
Titel der Buchreihe
Computational Synthesis and Creative Systems
Copyright
2020
Verlag
Springer International Publishing
Copyright Inhaber
Springer Nature Switzerland AG
eBook ISBN
978-3-319-70163-9
DOI
10.1007/978-3-319-70163-9
Hardcover ISBN
978-3-319-70162-2
Buchreihen ISSN
2509-6575
Auflage
1
Seitenzahl
XXVIII, 284
Anzahl der Bilder
52 schwarz-weiß Abbildungen, 91 Abbildungen in Farbe
Themen