Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems)

Briot

ISBN 10: 3319701622 / ISBN 13: 9783319701622

Edité par Springer (edition 1st ed. 2020), 2019

A propos de cet article

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N° de réf. du vendeur 3319701622-11-1

Détails bibliographiques

• Titre: Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems)
• Éditeur: Springer (edition 1st ed. 2020)
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 3319701622
• ISBN à 13 chiffres: 9783319701622
• Reliure: Hardcover
• Édition: 1st ed. 2020.
• État de l'article: Very Good

A propos de ce titre

Synopsis

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.