This tutorial aims to provide an overview of generative adversarial networks (GANs) and their use in generating music. The format of the tutorial will include lectures, demonstration of sample systems and technical results with illustrative musical examples.
We will start by discussing the scope of music generation and introduce various tasks that can broadly be regarded as music generation. For each task, we will then discuss its challenges, commonly used approaches and some notable systems proposed in the literature.
In the second part, we will explain the machine learning fundamentals for GANs. We will also present some interesting applications of GANs in other fields to showcase their potentials.
The following section will contain the case studies of four different tasks—symbolic melody generation, symbolic arrangement generation, symbolic musical style transfer and musical audio generation. In each part, we will first provide an overview of the task and then introduce several models proposed in the literature as examples.
We will conclude the tutorial by discussing the current limitations of GAN-based models and suggesting some possible future research directions.
In addition to lectures, we will go through some demo projects using Google Colab. These demo projects are designed to provide participants with hands-on experience and deeper understanding of the training of GANs. We will also cover topics such as data representation, processing, I/O, visualization and evaluation.
The tutorial is targeted to students and newcomers who are interested in or working on music generation research, and also machine learning specialists who want to see how GANs can be applied to music generation.