![]() For our purposes, a note can be described by its pitch and duration. NotesĪ note is a symbolic representation of a sound. We will focus on MIDI files and will extract two types of symbolic objects: notes and chords. Common discrete forms include Musical Instrument Digital Interface (MIDI) files, pianoroll, and text. We will not use continuous forms in this tutorial, but you can read more about them in the Appendix. The most common continuous form is an audio signal, typically stored as a WAV file. ![]() For music, data can be represented using either a continuous or discrete form. Generative Music Representationīefore we can define and train a generative model, we must first assemble a dataset. In this tutorial, we'll make use of generative models to compose music. A generative model might compose songs of a particular genre. Generative models create new instances of a class.Ī discriminative model of music could be used to classify songs into different genres. Discriminative models identify a decision boundary and produce a corresponding classification. Supervised machine learning models can be divided into two categories: discriminative models and generative models. For an exhaustive review of the deep learning for music literature, see Briot, Hadjerest, and Pachet (2019), which we will refer to throughout this tutorial. For those who would like to learn more about TensorFlow 2.0, see Introduction to TensorFlow in Python on DataCamp. This tutorial was developed around TensorFlow 2.0 in Python, along with the high-level Keras API, which plays an enhanced role in TensorFlow 2.0.
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