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Deep learning techniques for music g...

Briot, Jean-Pierre.

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Deep learning techniques for music generation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning techniques for music generation/ by Jean-Pierre Briot, Gaetan Hadjeres, Francois-David Pachet.
Author:	Briot, Jean-Pierre.
other author:	Hadjeres, Gaetan.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xxviii, 284 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Method -- Objective -- Representation -- Architecture -- Challenge and Strategy -- Analysis -- Discussion and Conclusion.
Contained By:	Springer eBooks
Subject:	Computer music. -
Online resource:	https://doi.org/10.1007/978-3-319-70163-9
ISBN:	9783319701639

Deep learning techniques for music generation
Briot, Jean-Pierre.

Deep learning techniques for music generation[electronic resource] /by Jean-Pierre Briot, Gaetan Hadjeres, Francois-David Pachet. - Cham :Springer International Publishing :2020. - xxviii, 284 p. :ill., digital ;24 cm. - Computational synthesis and creative systems,2509-6575. - Computational synthesis and creative systems..

Introduction -- Method -- Objective -- Representation -- Architecture -- Challenge and Strategy -- Analysis -- Discussion and Conclusion.

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.

ISBN: 9783319701639

Standard No.: 10.1007/978-3-319-70163-9doiSubjects--Topical Terms:

603912
Computer music.

LC Class. No.: M1473 / .B75 2020

Dewey Class. No.: 781.34

Deep learning techniques for music generation
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245 1 0 $a Deep learning techniques for music generation $h [electronic resource] / $c by Jean-Pierre Briot, Gaetan Hadjeres, Francois-David Pachet.
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300 $a xxviii, 284 p. : $b ill., digital ; $c 24 cm.
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505 0 $a Introduction -- Method -- Objective -- Representation -- Architecture -- Challenge and Strategy -- Analysis -- Discussion and Conclusion.
520 $a 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.
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