Titlebook: Deep Learning Techniques for Music Generation; Jean-Pierre Briot,Ga?tan Hadjeres,Fran?ois-David P Book 2020 Springer Nature Switzerland AG

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魅力

Antarctic

才能

Representation,The second dimension of our analysis, the ., is about the way the musical content is represented. The choice of representation and its encoding is tightly connected to the configuration of the input and the output of the architecture, i.e. the number of input and output variables as well as their corresponding types.

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exigent

Challenge and Strategy,We are now reaching the core of this book. This chapter will analyze in depth how to apply the architectures presented in Chapter 5 to learn and generate music. We will first start with a naive, straightforward strategy, using the basic prediction task of a neural network to generate an accompaniment for a melody.

nostrum

陳腐思想

Adjourn

Introduction,voice recognition or translation. It became popular in 2012, when a deep learning architecture significantly outperformed standard techniques relying on handcrafted features in an image classification competition, see more details in Section 5.

mechanical

Conceptual Elements of Framework,voice recognition or translation. It became popular in 2012, when a deep learning architecture significantly outperformed standard techniques relying on handcrafted features in an image classification competition, see more details in Section 5.