LEADER 04487nam 22006615 450 001 9910366657603321 005 20200730120144.0 010 $a9783319701639 010 $a3319701630 024 7 $a10.1007/978-3-319-70163-9 035 $a(CKB)4100000009759007 035 $a(DE-He213)978-3-319-70163-9 035 $a(MiAaPQ)EBC5975935 035 $a(PPN)260303739 035 $a(EXLCZ)994100000009759007 100 $a20191108d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning Techniques for Music Generation /$fby Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXVIII, 284 p. 143 illus., 91 illus. in color.) 225 1 $aComputational Synthesis and Creative Systems,$x2509-6575 311 08$a9783319701622 311 08$a3319701622 327 $aIntroduction -- Method -- Objective -- Representation -- Architecture -- Challenge and Strategy -- Analysis -- Discussion and Conclusion. 330 $aThis book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website. 410 0$aComputational Synthesis and Creative Systems,$x2509-6575 606 $aArtificial intelligence 606 $aMusic 606 $aApplication software 606 $aMathematics 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMusic$3https://scigraph.springernature.com/ontologies/product-market-codes/417000 606 $aComputer Appl. in Arts and Humanities$3https://scigraph.springernature.com/ontologies/product-market-codes/I23036 606 $aMathematics in Music$3https://scigraph.springernature.com/ontologies/product-market-codes/M33000 615 0$aArtificial intelligence. 615 0$aMusic. 615 0$aApplication software. 615 0$aMathematics. 615 14$aArtificial Intelligence. 615 24$aMusic. 615 24$aComputer Appl. in Arts and Humanities. 615 24$aMathematics in Music. 676 $a006.3 700 $aBriot$b Jean-Pierre$4aut$4http://id.loc.gov/vocabulary/relators/aut$0861273 702 $aHadjeres$b Gaëtan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPachet$b François-David$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910366657603321 996 $aDeep Learning Techniques for Music Generation$91922211 997 $aUNINA