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000923376 019__ $$a1129157179
000923376 020__ $$a9783319701639$$q(electronic book)
000923376 020__ $$a3319701630$$q(electronic book)
000923376 020__ $$z9783319701622
000923376 0247_ $$a10.1007/978-3-319-70163-9$$2doi
000923376 0247_ $$a10.1007/978-3-319-70
000923376 035__ $$aSP(OCoLC)on1127580057
000923376 035__ $$aSP(OCoLC)1127580057$$z(OCoLC)1129157179
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000923376 049__ $$aISEA
000923376 050_4 $$aM1473
000923376 08204 $$a781.3/4$$223
000923376 1001_ $$aBriot, Jean-Pierre,$$eauthor.
000923376 24510 $$aDeep learning techniques for music generation /$$cJean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet.
000923376 264_1 $$aCham, Switzerland :$$bSpringer,$$c2020.
000923376 300__ $$a1 online resource (xxviii, 284 pages) :$$billustrations.
000923376 336__ $$atext$$btxt$$2rdacontent
000923376 337__ $$acomputer$$bc$$2rdamedia
000923376 338__ $$aonline resource$$bcr$$2rdacarrier
000923376 4901_ $$aComputational Synthesis and Creative Systems,$$x2509-6575
000923376 504__ $$aIncludes bibliographical references and index.
000923376 5050_ $$aIntroduction -- Method -- Objective -- Representation -- Architecture -- Challenge and Strategy -- Analysis -- Discussion and Conclusion.
000923376 506__ $$aAccess limited to authorized users.
000923376 520__ $$aThis 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.
000923376 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 14, 2019).
000923376 650_0 $$aComputer music.
000923376 650_0 $$aMachine learning.
000923376 7001_ $$aHadjeres, Gaëtan,$$eauthor.
000923376 7001_ $$aPachet, François,$$eauthor.
000923376 830_0 $$aComputational synthesis and creative systems.
000923376 852__ $$bebk
000923376 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-70163-9$$zOnline Access$$91397441.1
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000923376 980__ $$aBIB
000923376 982__ $$aEbook
000923376 983__ $$aOnline
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