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.
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