How can you specialize in generative AI for music?

1 Learn the basics of AI

Before you dive into generative AI, you need to have a solid foundation in the core concepts and techniques of artificial intelligence. You should be familiar with topics such as machine learning, deep learning, neural networks, natural language processing, computer vision, and data analysis. You should also be proficient in programming languages such as Python, R, or MATLAB, and frameworks such as TensorFlow, PyTorch, or Keras. There are many online courses, books, and tutorials that can help you learn the basics of AI.

2 Explore generative models

Generative models are a type of neural network that can produce new data from existing data. There are different types of generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and autoregressive models (ARs). Each of these models has its own advantages and disadvantages, and you should explore how they work and what they can do. You should also learn how to use libraries and tools that can help you implement generative models, such as Magenta, Jukebox, or MuseNet.

3 Study music theory and composition

To generate music with AI, you need to understand the elements and principles of music theory and composition. You should know how to read and write music notation, how to use scales, chords, and melodies, how to structure a song, and how to apply musical styles and genres. You should also listen to and analyze a variety of music, from classical to pop, and learn how to identify and reproduce the features and patterns that make them unique. You can use online resources, books, or courses to study music theory and composition.

4 Experiment with datasets and projects

The best way to learn and improve your skills in generative AI for music is to experiment with different datasets and projects. You can find and download datasets of music from various sources, such as MIDI files, audio files, or lyrics. You can also create your own datasets by recording or synthesizing music. You can then use generative models to train and test your algorithms on these datasets, and evaluate the results. You can also try to generate music for specific purposes or audiences, such as games, films, or podcasts.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?