9783319701622 - deep learning techniques for music generation (computational synthesis and creative systems) de briot (21 resultados)
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Condición: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.
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Hardback. Condición: 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 type…s 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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Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems)
Briot, Jean-Pierre, Hadjeres, Gaëtan, Pachet, François-David
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Serie: Libro 5 de 7 - Computational Synthesis and Creative Systems
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Hardcover. Condición: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Buch. Condición: 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 t…o 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.
Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems)
Briot, Jean-Pierre/ Hadjeres, Gaëtan/ Pachet, François-David
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Serie: Libro 5 de 7 - Computational Synthesis and Creative Systems
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Hardcover. Condición: Brand New. 312 pages. 9.25x6.10x0.87 inches. In Stock.
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Serie: Libro 5 de 7 - Computational Synthesis and Creative Systems
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Condición: New.
Idioma: Inglés
Editorial: Springer International Publishing AG, CH, 2019
Serie: Libro 5 de 7 - Computational Synthesis and Creative Systems
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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Hardback. Condición: 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 type…s 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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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 conceptual…framework 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. 312 pp. Englisch.
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Librería: moluna, Greven, Alemaniamoluna
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Condición: 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 F…low Machines project.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 312 pp. Englisch.
