Deep Learning Techniques for Music Generation

04/14/2020
by   Jean-Pierre Briot, et al.
0

This book is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: - Objective -- What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. – For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). - Representation – What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. – What format is to be used? Examples are: MIDI, piano roll or text. – How will the representation be encoded? Examples are: scalar, one-hot or many-hot. - Architecture – What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. - Challenge – What are the limitations and open challenges? Examples are: variability, interactivity and creativity. - Strategy – How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described in this book and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last part of this book includes some discussion and some prospects. A table of contents, a list of tables, a list of figures, a table of acronyms, a bibliography, a glossary and an index complete this book. Supplementary material is provided at the following companion web site: www.briot.info/dlt4mg/

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

09/05/2017

Deep Learning Techniques for Music Generation - A Survey

This book is a survey and an analysis of different ways of using deep le...
12/09/2017

Music Generation by Deep Learning - Challenges and Directions

In addition to traditional tasks such as prediction, classification and ...
04/07/2020

From Artificial Neural Networks to Deep Learning for Music Generation – History, Concepts and Trends

The current tsunami of deep learning (the hyper-vitamined return of arti...
11/13/2020

A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions

The utilization of deep learning techniques in generating various conten...
10/31/2017

A SeqGAN for Polyphonic Music Generation

We propose an application of SeqGAN, generative adversarial networks for...
12/12/2016

A Unit Selection Methodology for Music Generation Using Deep Neural Networks

Several methods exist for a computer to generate music based on data inc...
12/15/2019

Wykorzystanie sztucznej inteligencji do generowania treści muzycznych

This thesis is presenting a method for generating short musical phrases ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.