Can I read Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems) on EtoBox?

Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems) by Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet is a nonfiction available to read on EtoBox.

What is Deep Learning Techniques for Music Generation (Computational Synthesis and Creative Systems) about?

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,

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

Author

Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet

Publisher

Springer International Publishing, Imprint Springer

Published

2020

Language

EN

ISBN

9783319701639

Category

nonfiction

Subjects

Science, Mathematics, Music

Updated

2026-03-24