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The current wave of deep learning (the hyper-vitamined return of artificialneural networks) applies not only to traditional statistical machine learningtasks: prediction and classification (e.g., for weather prediction and patternrecognition), but has already conquered other areas, such as translation. Agrowing area of application is the generation of creative content, notably thecase of music, the topic of this paper. The motivation is in using the capacityof modern deep learning techniques to automatically learn musical styles fromarbitrary musical corpora and then to generate musical samples from theestimated distribution, with some degree of control over the generation. Thispaper provides a tutorial on music generation based on deep learningtechniques. After a short introduction to the topic illustrated by a recentexemple, the paper analyzes some early works from the late 1980s usingartificial neural networks for music generation and how their pioneeringcontributions have prefigured current techniques. Then, we introduce someconceptual framework to analyze the various concepts and dimensions involved.Various examples of recent systems are introduced and analyzed to illustratethe variety of concerns and of techniques.

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