Jean-Pierre Briot; Ga?tan Hadjeres; Fran?ois Pache Deep Learning Techniques for Music Generation 9783319701622

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Автор: Jean-Pierre Briot; Ga?tan Hadjeres; Fran?ois Pache
Название: Deep Learning Techniques for Music Generation
ISBN: 9783319701622
Издательство: Springer
Классификация:

Музыка

Гуманитарные науки

Военные действия и оборона

Прикладная математика

Искусственный интеллект

ISBN-10: 3319701622
Обложка/Формат: Hardcover
Вес: 0.63 кг.
Дата издания: 20.11.2019
Серия: Computational Synthesis and Creative Systems
Язык: English
Издание: 1st ed. 2019
Иллюстрации: 91 illustrations, color; 52 illustrations, black and white; xxviii, 284 p. 143 illus., 91 illus. in color.
Размер: 162 x 242 x 18
Читательская аудитория: General (us: trade)
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.
Дополнительное описание: Introduction.- Method.- Objective.- Representation.- Architecture.- Challenge and Strategy.- Analysis.- Discussion and Conclusion.

Deep Learning Techniques for Music Generation, Jean-Pierre Briot; Ga?tan Hadjeres; Fran?ois Pache