000 | 04143nam a22005895i 4500 | ||
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001 | 978-3-319-70163-9 | ||
003 | DE-He213 | ||
005 | 20240423125242.0 | ||
007 | cr nn 008mamaa | ||
008 | 191108s2020 sz | s |||| 0|eng d | ||
020 |
_a9783319701639 _9978-3-319-70163-9 |
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024 | 7 |
_a10.1007/978-3-319-70163-9 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aBriot, Jean-Pierre. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aDeep Learning Techniques for Music Generation _h[electronic resource] / _cby Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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300 |
_aXXVIII, 284 p. 143 illus., 91 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aComputational Synthesis and Creative Systems, _x2509-6583 |
|
505 | 0 | _aIntroduction -- Method -- Objective -- Representation -- Architecture -- Challenge and Strategy -- Analysis -- Discussion and Conclusion. | |
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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aMusic. | |
650 | 0 | _aDigital humanities. | |
650 | 0 |
_aMusic _xMathematics. |
|
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aMusic. |
650 | 2 | 4 | _aDigital Humanities. |
650 | 2 | 4 | _aMathematics in Music. |
700 | 1 |
_aHadjeres, Gaëtan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
700 | 1 |
_aPachet, François-David. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319701622 |
776 | 0 | 8 |
_iPrinted edition: _z9783319701646 |
830 | 0 |
_aComputational Synthesis and Creative Systems, _x2509-6583 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-70163-9 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c175909 _d175909 |