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Synopsis
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.Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems)
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Deep Learning Techniques for Music Generation
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Deep Learning Techniques for Music Generation
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Springer International Publishing AG, CH, 2019
ISBN 10 : 3319701622
ISBN 13 : 9783319701622
Neuf
Couverture rigide
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Hardback. Etat : New. 2020 ed. 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. N° de réf. du vendeur LU-9783319701622
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Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems)
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Springer, 2020
ISBN 10 : 3319701622
ISBN 13 : 9783319701622
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Hardcover. Etat : Very Good. No Jacket. 1st Edition. 2020.Hardcover.Very good condition.284 pages.Ships from Japan.Usually ships in 1-2 working days. N° de réf. du vendeur 34327
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Deep Learning Techniques for Music Generation
Edité par
Springer International Publishing AG, CH, 2019
ISBN 10 : 3319701622
ISBN 13 : 9783319701622
Neuf
Couverture rigide
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
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Hardback. Etat : New. 2020 ed. 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. N° de réf. du vendeur LU-9783319701622
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Deep Learning Techniques for Music Generation
Edité par
Springer, 2019
ISBN 10 : 3319701622
ISBN 13 : 9783319701622
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Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Deep Learning Techniques for Music Generation
Edité par
Springer, 2019
ISBN 10 : 3319701622
ISBN 13 : 9783319701622
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Deep Learning Techniques for Music Generation
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Deep Learning Techniques for Music Generation
Edité par
Springer International Publishing, 2019
ISBN 10 : 3319701622
ISBN 13 : 9783319701622
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Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Authors analysis based on five dimensions: objective, representation, architecture, challenge, and strategyImportant application of deep learning, for AI researchers and composersResearch was conducted within the EU Flow Machines project. N° de réf. du vendeur 174376061
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Deep Learning Techniques for Music Generation
Edité par
Springer International Publishing, 2019
ISBN 10 : 3319701622
ISBN 13 : 9783319701622
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This bookis a survey and analysis of how deep learning can be used to generate musicalcontent. The authors offer a comprehensive presentation of the foundations ofdeep learningtechniques for music generation. They also develop a conceptualframework used to classify and analyze various types of architecture, encodingmodels, generation strategies, and ways tocontrol the generation. The five dimensionsof this framework are: objective (the kind of musical content to be generated, e.g.,melody, accompaniment); representation (the musicalelements to be considered andhow to encode them, e.g., chord, silence, piano roll, one-hot encoding);architecture (the structure organizing neurons, their connexions, and the flowof theiractivations, e.g., feedforward, recurrent, variational autoencoder);challenge (the desired properties and issues, e.g., variability,incrementality, adaptability); and strategy (the way to modeland control theprocess of generation, e.g., single-step feedforward, iterative feedforward,decoder feedforward, sampling). To illustrate the possible design decisions andto allowcomparison and correlation analysis they analyze and classify morethan 40 systems, and they discuss important open challenges such as interactivity,originality, and structure. The authorshave extensive knowledge and experience in all related research, technical,performance, and business aspects. The book is suitable for students,practitioners, andresearchersin the artificial intelligence, machine learning, and music creation domains.The reader does not require any prior knowledge about artificial neuralnetworks, deep learning, orcomputer music. The text is fully supported with acomprehensive table of acronyms, bibliography, glossary, and index, andsupplementary material is available from the authors' website. N° de réf. du vendeur 9783319701622
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