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.
