Authors:
- 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
Part of the book series: Computational Synthesis and Creative Systems (CSACS)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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LIP6, Sorbonne Université, CNRS, Paris, France
Jean-Pierre Briot
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Sony Computer Science Laboratories, Paris, France
Gaëtan Hadjeres
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Spotify Creator Technology Research Lab, Paris, France
François-David Pachet
Bibliographic Information
Book Title: Deep Learning Techniques for Music Generation
Authors: Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet
Series Title: Computational Synthesis and Creative Systems
DOI: https://doi.org/10.1007/978-3-319-70163-9
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-319-70162-2Published: 20 November 2019
eBook ISBN: 978-3-319-70163-9Published: 08 November 2019
Series ISSN: 2509-6575
Series E-ISSN: 2509-6583
Edition Number: 1
Number of Pages: XXVIII, 284
Number of Illustrations: 52 b/w illustrations, 91 illustrations in colour
Topics: Artificial Intelligence, Music, Computer Appl. in Arts and Humanities, Mathematics in Music