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

Abstract : The current tsunami of deep learning (the hyper-vitamined return of artificial neural networks) applies not only to traditional statistical machine learning tasks: prediction and classification (e.g., for weather prediction and pattern recognition), but has already conquered other areas, such as translation. A growing area of application is the generation of creative content: in particular the case of music, the topic of this paper. The motivation is in using the capacity of modern deep learning techniques to automatically learn musical styles from arbitrary musical corpora and then to generate musical samples from the estimated distribution, with some degree of control over the generation. This article provides a survey of music generation based on deep learning techniques. After a short introduction to the topic illustrated by a recent exemple, the article analyses some early works from the late 1980s using artificial neural networks for music generation and how their pioneering contributions foreshadowed current techniques. Then, we introduce some conceptual framework to analyze the various concepts and dimensions involved. Various examples of recent systems are introduced and analyzed to illustrate the variety of concerns and of techniques.
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https://hal.sorbonne-universite.fr/hal-02539189
Contributor : Jean-Pierre Briot <>
Submitted on : Thursday, October 8, 2020 - 4:12:55 PM
Last modification on : Thursday, October 22, 2020 - 9:08:41 PM

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Jean-Pierre Briot. From Artificial Neural Networks to Deep Learning for Music Generation - History, Concepts and Trends. Neural Computing and Applications, Springer Verlag, 2020, ⟨10.1007/s00521-020-05399-0⟩. ⟨hal-02539189v3⟩

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