This book is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. At first, we propose a methodology based on four dimensions for our analysis: - objective - What musical content is to be generated? (e.g., melody, accompaniment…); - representation - What are the information formats used for the corpus and for the expected generated output? (e.g., MIDI, piano roll, text…); - architecture - What type of deep neural network is to be used? (e.g., recurrent network, autoencoder, generative adversarial networks…); - strategy - How to model and control the process of generation (e.g., direct feedforward, sampling, unit selection…). For each dimension, we conduct a comparative analysis of various models and techniques. For the strategy dimension, we propose some tentative typology of possible approaches and mechanisms. This classification is bottom-up, based on the analysis of many existing deep-learning based systems for music generation, which are described in this book
via https://arxiv.org/abs/1709.01620