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Record Nr. |
UNINA9910366657603321 |
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Autore |
Briot Jean-Pierre |
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Titolo |
Deep Learning Techniques for Music Generation / / by Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XXVIII, 284 p. 143 illus., 91 illus. in color.) |
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Collana |
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Computational Synthesis and Creative Systems, , 2509-6575 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Music |
Application software |
Mathematics |
Artificial Intelligence |
Computer Appl. in Arts and Humanities |
Mathematics in Music |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- Method -- Objective -- Representation -- Architecture -- Challenge and Strategy -- Analysis -- Discussion and Conclusion. |
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Sommario/riassunto |
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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, |
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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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